{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17},"id":"8r4P8MeYCVUY","executionInfo":{"status":"ok","timestamp":1687184830512,"user_tz":-480,"elapsed":9,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"5eb23a67-aab8-4f8b-8665-7c12f813fdde"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<style>.container { width:100% !important; }</style>"]},"metadata":{}}],"source":["from IPython.core.display import display, HTML\n","display(HTML(\"<style>.container { width:100% !important; }</style>\"))"]},{"cell_type":"code","source":[],"metadata":{"id":"MLsXn8kzJBjA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Introduction Pymoo: An Multi-objective Optimization Toolbox in Python\n"],"metadata":{"id":"BZDyohPNDVVP"}},{"cell_type":"code","source":["# installation\n","!pip install -U pymoo"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Aj8WFOBDbpZ","executionInfo":{"status":"ok","timestamp":1687201709814,"user_tz":-480,"elapsed":8262,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"5ceb1d03-75c9-4d70-e9ae-3f851e8e89dd"},"execution_count":82,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: pymoo in /usr/local/lib/python3.10/dist-packages (0.6.0.1)\n","Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.10/dist-packages (from pymoo) (1.22.4)\n","Requirement already satisfied: scipy>=1.1 in /usr/local/lib/python3.10/dist-packages (from pymoo) (1.10.1)\n","Requirement already satisfied: matplotlib>=3 in /usr/local/lib/python3.10/dist-packages (from pymoo) (3.7.1)\n","Requirement already satisfied: autograd>=1.4 in /usr/local/lib/python3.10/dist-packages (from pymoo) (1.5)\n","Requirement already satisfied: cma==3.2.2 in /usr/local/lib/python3.10/dist-packages (from pymoo) (3.2.2)\n","Requirement already satisfied: alive-progress in /usr/local/lib/python3.10/dist-packages (from pymoo) (3.1.4)\n","Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from pymoo) (0.3.6)\n","Requirement already satisfied: Deprecated in /usr/local/lib/python3.10/dist-packages (from pymoo) (1.2.14)\n","Requirement already satisfied: future>=0.15.2 in /usr/local/lib/python3.10/dist-packages (from autograd>=1.4->pymoo) (0.18.3)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (1.0.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (0.11.0)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (4.39.3)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (1.4.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (23.1)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (8.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (3.0.9)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3->pymoo) (2.8.2)\n","Requirement already satisfied: about-time==4.2.1 in /usr/local/lib/python3.10/dist-packages (from alive-progress->pymoo) (4.2.1)\n","Requirement already satisfied: grapheme==0.6.0 in /usr/local/lib/python3.10/dist-packages (from alive-progress->pymoo) (0.6.0)\n","Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from Deprecated->pymoo) (1.14.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3->pymoo) (1.16.0)\n"]}]},{"cell_type":"markdown","source":["![](https://raw.githubusercontent.com/mikelzc1990/nasbench101/master/pymoo_arch.png)"],"metadata":{"id":"v53cHU9oFvBx"}},{"cell_type":"code","source":["import numpy as np\n","from pymoo.optimize import minimize\n","from pymoo.problems import get_problem\n","from pymoo.decomposition.asf import ASF\n","from pymoo.algorithms.moo.nsga2 import NSGA2\n","from pymoo.algorithms.moo.moead import MOEAD\n","from pymoo.visualization.scatter import Scatter\n","from pymoo.mcdm.high_tradeoff import HighTradeoffPoints\n","from pymoo.util.ref_dirs import get_reference_directions"],"metadata":{"id":"1gRZL1sWGaC5","executionInfo":{"status":"ok","timestamp":1687201738240,"user_tz":-480,"elapsed":453,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}}},"execution_count":83,"outputs":[]},{"cell_type":"markdown","source":["## An Example Bi-objective Problem (Welded Beam)\n","\n","![](https://raw.githubusercontent.com/mikelzc1990/nasbench101/master/image15.png)"],"metadata":{"id":"t2MEHhc0GhM1"}},{"cell_type":"code","source":["# First define the problem\n","\n","# problem = get_problem(\"welded_beam\")\n","problem = get_problem(\"tnk\")\n","\n","print(\"Number of decision variables: {}\".format(problem.n_var))\n","print(\"Number of objectives: {}\".format(problem.n_obj))\n","print(\"Number of constraints: {}\".format(problem.n_constr))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oQzLRWEiKmXX","executionInfo":{"status":"ok","timestamp":1687201910308,"user_tz":-480,"elapsed":457,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"1f5dd5e4-829e-4767-ee62-075096dcc33a"},"execution_count":87,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of decision variables: 2\n","Number of objectives: 2\n","Number of constraints: 2\n"]}]},{"cell_type":"code","source":["# Then define the algorithm, e.g., NSGA-II, MOEA/D, etc.\n","\n","## NSGA-II\n","algorithm = NSGA2(pop_size=100)\n","# ## MOEA/D\n","# ref_dirs = get_reference_directions(\"das-dennis\", 2, n_partitions=99)\n","# algorithm = MOEAD(ref_dirs)"],"metadata":{"id":"kJorWCg1LBWf","executionInfo":{"status":"ok","timestamp":1687201917372,"user_tz":-480,"elapsed":436,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}}},"execution_count":88,"outputs":[]},{"cell_type":"code","source":["# Kick-off the optimization\n","res = minimize(problem,\n","               algorithm,\n","               ('n_gen', 200),\n","               seed=1,\n","               verbose=True)\n","\n","plot = Scatter()\n","plot.add(problem.pareto_front(), plot_type=\"line\", color=\"black\", alpha=0.7)\n","plot.add(res.F, facecolor=\"none\", edgecolor=\"red\")\n","plot.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"drG2mwunF3HD","executionInfo":{"status":"ok","timestamp":1687201923264,"user_tz":-480,"elapsed":4713,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"598d6344-d3f0-431d-fc92-90ca1da569e6"},"execution_count":89,"outputs":[{"output_type":"stream","name":"stdout","text":["==========================================================================================================\n","n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      igd      |       gd      |       hv     \n","==========================================================================================================\n","     1 |      100 |      2 |  0.000000E+00 |  7.0592493221 |  0.4207731496 |  0.1391529706 |  0.0767719243\n","     2 |      200 |      4 |  0.000000E+00 |  1.1737816796 |  0.2153340993 |  0.0856399030 |  0.1238055108\n","     3 |      300 |      7 |  0.000000E+00 |  0.2158393232 |  0.1573338822 |  0.1078738521 |  0.1586127279\n","     4 |      400 |     11 |  0.000000E+00 |  0.0399574099 |  0.1077729238 |  0.0606239231 |  0.1879251415\n","     5 |      500 |     10 |  0.000000E+00 |  0.000000E+00 |  0.1034253367 |  0.0245026686 |  0.2270864292\n","     6 |      600 |     17 |  0.000000E+00 |  0.000000E+00 |  0.0573729631 |  0.0348180664 |  0.2465330950\n","     7 |      700 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0389164505 |  0.0298402867 |  0.2666282843\n","     8 |      800 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0362086942 |  0.0243318553 |  0.2693435146\n","     9 |      900 |     21 |  0.000000E+00 |  0.000000E+00 |  0.0323577883 |  0.0216267299 |  0.2727179989\n","    10 |     1000 |     23 |  0.000000E+00 |  0.000000E+00 |  0.0279022140 |  0.0154137757 |  0.2760586852\n","    11 |     1100 |     29 |  0.000000E+00 |  0.000000E+00 |  0.0258819960 |  0.0182630158 |  0.2772973379\n","    12 |     1200 |     31 |  0.000000E+00 |  0.000000E+00 |  0.0255631205 |  0.0194316973 |  0.2786810671\n","    13 |     1300 |     32 |  0.000000E+00 |  0.000000E+00 |  0.0252473153 |  0.0195355567 |  0.2794563790\n","    14 |     1400 |     34 |  0.000000E+00 |  0.000000E+00 |  0.0237075936 |  0.0167544563 |  0.2816594593\n","    15 |     1500 |     31 |  0.000000E+00 |  0.000000E+00 |  0.0226210302 |  0.0144986882 |  0.2825883410\n","    16 |     1600 |     37 |  0.000000E+00 |  0.000000E+00 |  0.0215125818 |  0.0162839009 |  0.2854946731\n","    17 |     1700 |     43 |  0.000000E+00 |  0.000000E+00 |  0.0198573276 |  0.0151467613 |  0.2867314424\n","    18 |     1800 |     45 |  0.000000E+00 |  0.000000E+00 |  0.0192076256 |  0.0144519099 |  0.2880736219\n","    19 |     1900 |     47 |  0.000000E+00 |  0.000000E+00 |  0.0166006947 |  0.0126157252 |  0.2902752618\n","    20 |     2000 |     48 |  0.000000E+00 |  0.000000E+00 |  0.0162618344 |  0.0122524159 |  0.2907618796\n","    21 |     2100 |     49 |  0.000000E+00 |  0.000000E+00 |  0.0159610391 |  0.0121085246 |  0.2909592264\n","    22 |     2200 |     51 |  0.000000E+00 |  0.000000E+00 |  0.0146931652 |  0.0108426818 |  0.2928130398\n","    23 |     2300 |     52 |  0.000000E+00 |  0.000000E+00 |  0.0144690294 |  0.0105774524 |  0.2929722976\n","    24 |     2400 |     55 |  0.000000E+00 |  0.000000E+00 |  0.0139300069 |  0.0103753042 |  0.2935779043\n","    25 |     2500 |     58 |  0.000000E+00 |  0.000000E+00 |  0.0136621877 |  0.0102216006 |  0.2938320418\n","    26 |     2600 |     58 |  0.000000E+00 |  0.000000E+00 |  0.0136479321 |  0.0102279397 |  0.2938333806\n","    27 |     2700 |     60 |  0.000000E+00 |  0.000000E+00 |  0.0128900633 |  0.0104391837 |  0.2940362408\n","    28 |     2800 |     61 |  0.000000E+00 |  0.000000E+00 |  0.0128117802 |  0.0104010336 |  0.2940861329\n","    29 |     2900 |     62 |  0.000000E+00 |  0.000000E+00 |  0.0128102825 |  0.0104974664 |  0.2940934359\n","    30 |     3000 |     62 |  0.000000E+00 |  0.000000E+00 |  0.0126787164 |  0.0103507937 |  0.2943011325\n","    31 |     3100 |     64 |  0.000000E+00 |  0.000000E+00 |  0.0112664022 |  0.0101107690 |  0.2948395996\n","    32 |     3200 |     63 |  0.000000E+00 |  0.000000E+00 |  0.0108069059 |  0.0095417552 |  0.2952750627\n","    33 |     3300 |     66 |  0.000000E+00 |  0.000000E+00 |  0.0107937149 |  0.0097372509 |  0.2955854325\n","    34 |     3400 |     68 |  0.000000E+00 |  0.000000E+00 |  0.0107057373 |  0.0095704817 |  0.2956879748\n","    35 |     3500 |     68 |  0.000000E+00 |  0.000000E+00 |  0.0107054178 |  0.0095569719 |  0.2956984398\n","    36 |     3600 |     70 |  0.000000E+00 |  0.000000E+00 |  0.0103005527 |  0.0093400163 |  0.2962012204\n","    37 |     3700 |     68 |  0.000000E+00 |  0.000000E+00 |  0.0102127954 |  0.0090568045 |  0.2965836375\n","    38 |     3800 |     68 |  0.000000E+00 |  0.000000E+00 |  0.0101873744 |  0.0090273895 |  0.2966128649\n","    39 |     3900 |     72 |  0.000000E+00 |  0.000000E+00 |  0.0099175909 |  0.0087834849 |  0.2968301969\n","    40 |     4000 |     73 |  0.000000E+00 |  0.000000E+00 |  0.0097625809 |  0.0087034934 |  0.2969814941\n","    41 |     4100 |     75 |  0.000000E+00 |  0.000000E+00 |  0.0094202898 |  0.0087520795 |  0.2972510162\n","    42 |     4200 |     76 |  0.000000E+00 |  0.000000E+00 |  0.0090361861 |  0.0086097721 |  0.2975143006\n","    43 |     4300 |     76 |  0.000000E+00 |  0.000000E+00 |  0.0089559957 |  0.0085523012 |  0.2976217810\n","    44 |     4400 |     74 |  0.000000E+00 |  0.000000E+00 |  0.0089824205 |  0.0086888571 |  0.2977390321\n","    45 |     4500 |     73 |  0.000000E+00 |  0.000000E+00 |  0.0089718278 |  0.0085386355 |  0.2977665837\n","    46 |     4600 |     75 |  0.000000E+00 |  0.000000E+00 |  0.0086364887 |  0.0083070811 |  0.2987425777\n","    47 |     4700 |     79 |  0.000000E+00 |  0.000000E+00 |  0.0082559360 |  0.0078923003 |  0.2990662149\n","    48 |     4800 |     79 |  0.000000E+00 |  0.000000E+00 |  0.0082430198 |  0.0078752968 |  0.2990856592\n","    49 |     4900 |     81 |  0.000000E+00 |  0.000000E+00 |  0.0082178531 |  0.0078318699 |  0.2991392170\n","    50 |     5000 |     83 |  0.000000E+00 |  0.000000E+00 |  0.0081970198 |  0.0081447538 |  0.2992185863\n","    51 |     5100 |     83 |  0.000000E+00 |  0.000000E+00 |  0.0080931216 |  0.0080123621 |  0.2993228983\n","    52 |     5200 |     81 |  0.000000E+00 |  0.000000E+00 |  0.0080095980 |  0.0078491387 |  0.2994539947\n","    53 |     5300 |     81 |  0.000000E+00 |  0.000000E+00 |  0.0078314597 |  0.0073509751 |  0.2997211336\n","    54 |     5400 |     82 |  0.000000E+00 |  0.000000E+00 |  0.0078133010 |  0.0073202803 |  0.2997216989\n","    55 |     5500 |     82 |  0.000000E+00 |  0.000000E+00 |  0.0076814324 |  0.0072584148 |  0.2999317543\n","    56 |     5600 |     84 |  0.000000E+00 |  0.000000E+00 |  0.0075471715 |  0.0073168690 |  0.3000272752\n","    57 |     5700 |     84 |  0.000000E+00 |  0.000000E+00 |  0.0075239156 |  0.0073051879 |  0.3000428223\n","    58 |     5800 |     83 |  0.000000E+00 |  0.000000E+00 |  0.0074727436 |  0.0071413010 |  0.3001212166\n","    59 |     5900 |     85 |  0.000000E+00 |  0.000000E+00 |  0.0072067982 |  0.0070552534 |  0.3002943251\n","    60 |     6000 |     86 |  0.000000E+00 |  0.000000E+00 |  0.0071347822 |  0.0073900225 |  0.3003356053\n","    61 |     6100 |     86 |  0.000000E+00 |  0.000000E+00 |  0.0070708338 |  0.0073124255 |  0.3004146600\n","    62 |     6200 |     87 |  0.000000E+00 |  0.000000E+00 |  0.0070041034 |  0.0072135654 |  0.3004447227\n","    63 |     6300 |     89 |  0.000000E+00 |  0.000000E+00 |  0.0067729989 |  0.0071278869 |  0.3005765410\n","    64 |     6400 |     90 |  0.000000E+00 |  0.000000E+00 |  0.0067204268 |  0.0070817898 |  0.3006183805\n","    65 |     6500 |     88 |  0.000000E+00 |  0.000000E+00 |  0.0066612858 |  0.0070450585 |  0.3006948492\n","    66 |     6600 |     89 |  0.000000E+00 |  0.000000E+00 |  0.0066560248 |  0.0070946131 |  0.3007090213\n","    67 |     6700 |     89 |  0.000000E+00 |  0.000000E+00 |  0.0066526616 |  0.0070410081 |  0.3007527911\n","    68 |     6800 |     90 |  0.000000E+00 |  0.000000E+00 |  0.0066431137 |  0.0069749859 |  0.3007963277\n","    69 |     6900 |     91 |  0.000000E+00 |  0.000000E+00 |  0.0066069511 |  0.0069527406 |  0.3008028215\n","    70 |     7000 |     92 |  0.000000E+00 |  0.000000E+00 |  0.0065941956 |  0.0069115681 |  0.3008132671\n","    71 |     7100 |     90 |  0.000000E+00 |  0.000000E+00 |  0.0065382063 |  0.0059510415 |  0.3008583048\n","    72 |     7200 |     90 |  0.000000E+00 |  0.000000E+00 |  0.0064405064 |  0.0058783837 |  0.3009087758\n","    73 |     7300 |     89 |  0.000000E+00 |  0.000000E+00 |  0.0063463417 |  0.0055420370 |  0.3009712271\n","    74 |     7400 |     91 |  0.000000E+00 |  0.000000E+00 |  0.0063221462 |  0.0056461881 |  0.3010141020\n","    75 |     7500 |     93 |  0.000000E+00 |  0.000000E+00 |  0.0062768174 |  0.0056843095 |  0.3010869363\n","    76 |     7600 |     94 |  0.000000E+00 |  0.000000E+00 |  0.0060585373 |  0.0058019660 |  0.3013323738\n","    77 |     7700 |     95 |  0.000000E+00 |  0.000000E+00 |  0.0059524592 |  0.0058637053 |  0.3014897177\n","    78 |     7800 |     97 |  0.000000E+00 |  0.000000E+00 |  0.0058904766 |  0.0059997470 |  0.3015625276\n","    79 |     7900 |     97 |  0.000000E+00 |  0.000000E+00 |  0.0058875037 |  0.0059769091 |  0.3015826166\n","    80 |     8000 |     99 |  0.000000E+00 |  0.000000E+00 |  0.0058152468 |  0.0059217660 |  0.3016614025\n","    81 |     8100 |     99 |  0.000000E+00 |  0.000000E+00 |  0.0058004204 |  0.0059223841 |  0.3016912376\n","    82 |     8200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0057923878 |  0.0058641755 |  0.3017059115\n","    83 |     8300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0057840988 |  0.0058624811 |  0.3017180048\n","    84 |     8400 |     98 |  0.000000E+00 |  0.000000E+00 |  0.0057279007 |  0.0058693573 |  0.3022952337\n","    85 |     8500 |     98 |  0.000000E+00 |  0.000000E+00 |  0.0057279007 |  0.0058693573 |  0.3022952337\n","    86 |     8600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0056650039 |  0.0058227738 |  0.3023449544\n","    87 |     8700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0056016361 |  0.0057927135 |  0.3023550011\n","    88 |     8800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0055709432 |  0.0059135633 |  0.3024335187\n","    89 |     8900 |     99 |  0.000000E+00 |  0.000000E+00 |  0.0055456052 |  0.0058869002 |  0.3024527067\n","    90 |     9000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0053143423 |  0.0056961262 |  0.3026135859\n","    91 |     9100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0053143423 |  0.0056961262 |  0.3026135859\n","    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200 |    20000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0032710604 |  0.0045205841 |  0.3050148301\n"]},{"output_type":"execute_result","data":{"text/plain":["<pymoo.visualization.scatter.Scatter at 0x7f3e5d57d4b0>"]},"metadata":{},"execution_count":89},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 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IE1ErgloR0s6yYPx4c3LZn3/eePzeezn7xx98/frrbNm4EdauJU9CAh2/+YYm3brhtmSJObEMIE8euHzZ9OOWKAFz50LVqna5HREREXFsakWQjGOzQe/eEBYGP/4I+fKZzWV585Lf1ZU3Y2IYtnUrBZOSuJg7N1/89BO9evTgl169SAb45BM4dw5efBECAyFvXmja1BwQMWYMVKsGQUGmF/frryEuzs43LCIiIlmBgq2kn5ub2QS2e7dpM9i504TQCROo/swzfFG1Kj0qV8bf35/TO3cy+tQperm4sKJ4cZJSUqBgQbMpbflyswpct66ZwFCypJmNGxAAL7xgWhaioux9tyIiIuLg1IqgVoS7JyUFKlY0o75WrTIzbFesIO7331lUowY/7t9PdJEiULkyAQEBPLFzJ01y5CDnpk1QvLhpa9i3D8qWvfE9t2+HJk2gWTOoXt2cihYYCJ06mX5fERERcXqpzWsKtgq2d9fu3dCokTng4aGHYPp0CA6GkBDifH1ZNm4c81eu5OKBA7B1K57BwTzYqBFNPvyQ+3LkwHb16s3fz7JMm8Lq1eDlZQ6MOHECrl6FAQNg5Eiz+Wz5cjOOLE8eeOQR8PW1y+2LiIjI3ZfavOaWiTVJdlCpEoSEwKhR16cmsG0b5MpFDjc3Hvfzo4WLC6u3b+en4sU5mT8/vy5bxq9Afm9vGs+aRcOGDSlYsKB57SefmFAL5gS0Nm0gOhrGjoU334STJ2HtWrPa6+YGSUkm1L7+ulkxttns8q9BspCkJFi0yPw59fAwfyGrUUN/dkREsiCt2GrFNmOlpJhA+t57cPSoeSwoCHr3xnr1Vf4IC2NV376sW7mS2HvvNfNtgYIFC1K5fHkqDxvGfQ89hP/cubBsGTRvfuN7P/EEzJ9vgsgHH8D990N4OIwebT6GDzcBV+R2tmyBJ580vwUoUsT8JuDCBahXD+bMgQIF7F2hiIigFVtxFC4u0K0bdO0KPXrAxIlQuzZUqYJt0ybKLlhA2XXreB7Y3KwZK7292b17NxEREUTs2sWy6GjYto17XF2puH8/Phcu4ObmhovNhtu2bbgCrtWr43buHK6rV+Pq6orbE0/geuYMru++i2vt2rjlyoWrq+v1Dzc3t9t+/f/Pubhof6XTOnrU/EWpQgVYsMCcjHflCqxcCX36wMMPm+Dr7m7vSkVEJJW0YqsV28xzbf7tqFFw5Ih5LCDABN7ffzeHOCxdSmzFiuzbt49dkyax66uvOObiAkWLmo1p11y5AmvWmH++/34oXPjmnxUXZzawVa5sVojTyWazpSoQpyYwu7i43DZE343n//6z/+t5Nzc3bDYbtuz86/YXX4R582D/fvjuO/jiC/PnEExf+LZt5vk2bexbp4iIaPNYainY2kFKigm2SUnmcAZPT7h0ybQUbN0KDRuayQi//QZ79nC5YkV2jxrFoRMnSEhIIDk5maSDB0keN45kILl3b5ILFiQpKYnk5GTzfFISyZ99RnKNGiRXqfLP5/76578/lpKSYu9/M5nuvwLznQTozAjzt3vexcXlv0N7/vzmNwnHj8MPP5jWllatICbGnI63ZYv5S1NoaOa8GSIiclsKtqmkYOtAEhLMCWTTpsHZs6bn8Y8/zPiwdevMIRDX7N0L991nxoQdPfrPjT6HD0Pp0jB7NrRvn6ofb1nWLQPv/4ff24Xku/H87X72f339Xz8nORseW/xvq+murq64TZqEa6lSuP7xB26PPkruGjXo1q0bhQsXNr9dKFPG/Dk6csT8BUxEROxGPbaS9Xh4mPm0nTrdeGz3brORp2pV6NvXTF3Yuxc+/xxcXc0khNhY8Pa+8RrLgnffNaO/Hn001T/eZrPh5uaGm5vz/WdhWRYpKSlOFdb//tit/Geg9/Iyp+flz29+i7B5M2FhYXz66af4e3mZdhcPD3P63ciRGfTOiIjI3aQVW63YOr4DB2DYMHN8b1KSCbSPP25WYp95xqzMvv66GdF09KiZiLBwofl18tNP27t6yWB/D+1pCuNTp5L84Yckd+xI4ksvMWnSJE6fPk25YsUYvm8fHtd6uN3cTEuCry/kzm3+svTkk+Y3CSIikinUipBKCrZZSGQknD9vNpxda0sICYFXXoENG25cV6YMvP8+tGtnlzIli4iLAx8f08by0kucrFWLAZ98wpXt26mXnMyA++/HdvKkaYux2czqbcWK5s9ciRLmUJBSpex9FyIi2UJq85pmGUnW4e8PJUve3GtbrRqsX29WdZctMzvZDxxQqJX/liOHWfX39obJkyncrh2vb9mCW0oK68qVY2WDBibUNm5s+mzvuceE299/N6u4LVpAYqK970JERP5GwVacQ9myZiZp9eo6MUpSb+BAcyhD3bowZw732Ww83aMH5M/PN2PHEuHhAT//bELtiBGwaRPEx5vDGw4dMvNvRUTEYSjYikj2VbmymVW7ejV06ACWxePr1nHfunXEJSfzadWqJHt6mmtbtjSfQ0PN66pUgSVL7Fa6iIj8k4KtiGRvrVqZo5h79gTAVq4c/caPxxs4GBfHDz/8YK6LjDSfrwVdHx8zok5ERByGgq2ISO7c8PHH1/u48/XsSc+iReHMGb777jvCwsJg8mTTY9u0KZw5Yw5wqF7d3pWLiMjfKNiKiIDZRNa3rxkXN2kSDV59lZqnT5MUHs6o3r1JGjrUbEr87Tfz2cMDunWzd9UiIvI3GvelcV8ick1yMjz/vFmdDQricnw8fc6eJQrokCMHneLiblzr7Q0DBsDQoWa2soiIZBiN+xIRSStXV/j2W9i+HR59lFw1a9KrQgWw2ZgXF8dJT0944gmz4axnT3jvPejVy95Vi4jIX5zv7FARkTtVrZr5AGoPHEi1I0cIqVOHL2rUYPjw4dhsNnjwQXOc84QJ0KWLGRkmIiJ2pRVbEZHbsSxsU6bQq2tXPPz92bNnD7/+9BM89RQULWpCLUDDhtCnj5mJKyIidqNgKyJyO4mJcO4cBR58kI4dO0JyMpO6dyd6+XIYNQr+/BPq1IFy5UxfbuvWpk9XRETsQsFWROR23N3NCLCDB3n88ccpGh1N1KVLTOvZ00xQCAw0M3Dr14cff4Tly2HxYntXLSKSbSnYiojcjs1m+mcnTsTt8mV6X7gAgYEs37uXwwcOwJgxcPy4aU146CEz13byZHtXLSKSbSnYioj8m9deMwG3fn3ujYigfsWKWAcO8FXlylgDBphr2rSBd9+FSpXg5En71isiko0p2IqI/JuiRWHtWvD1hcuXeWbVKnIcOsSBhAR+qVnTtB888YQZ/bVgAeTPb++KRUSyLQVbEZH/Uq4cbN4Mjz9OXqBDnTrQrBlTSpXiat268OWXJthevAhly9q7WhGRbEtzbEVEUis5GTw9eezIEVaULcupCxeYO3kyT589C59+alZ1d+2yd5UiItmWgq2ISGodOwZPPYXbmTM8u2QJ7wELli6lOVAAwMUFtmyBiAgoWNCupYqIZEdqRRARSS1/f9NusHgxwe++SxUg0deXKW3amEBbuTLExUHt2nD6tL2rFRHJdhRsRURSq317M6d2xw5sI0bwbMeO2Bo0YEN8PPtiYmD/fujXD65cgbfftne1IiLZjoKtiEhqde1qpiQ0awYJCRQfNYqHmjeHCxf4pk0brDx5YOBAc7zujBkQG2vvikVEshUFWxGR1PL1hdWrTS9tcjJUqkSnd97Ba9MmjiQm8svw4RAQYFoRYmLgzBl7Vywikq0o2IqIpEXx4vDCC+DjAz174t+rF+3fegsaNGDar78SFxcHJ06Ya/387FqqiEh2o2ArIpJW7dubPtoSJeCdd2j15psEBgZy8eJFfvj+e/jggxsrt7VqmaN3IyPtXbWIiNNTsBURSasKFaBjR+jdGyZMwD0xkWeeeQaio/nxhRc4f+QI5MsHLVpA4cKm77ZKFQgLs3flIiJOzWZZlmXvIuwpKioKf39/IiMj8dOvDUUkteLioEcPs0ksZ04sf3+GRESwD2jcuDGvrFp149pjx6BJEzMubPt2sNnsVbWISJaU2rymFVsRkfTIkQOmTYNDh+Cdd7B17053b28oWpRfvLwI+/vqbPHiMGEChIbChg12K1lExNkp2IqI3ImSJWHAAOjQgTIxMdR9+GEsy2Ly5Mk3X9eoEeTJA2vX2qdOEZFsQMFWRORu+Kur6+nmzXFzc2PHjh3s2LHjxvM2m1oQREQymIKtiMjdULYs5M9P4OrVtGzZEoCJEyeStGOHWdFt0gQuXDCbyUREJEMo2IqI3A0eHubEsa++or2PD74+PpxYuJDFVauaXtxNm8DNDbp3Nx9JSfauWETE6SjYiojcLUOGwKOP4tuxI11XroTwcGbmysXFixchf37Yuxe+/toE3TfesHe1IiJOR+O+NO5LRO6mlBSYPRurSxf6u7tzKEcOGpYty6tFi0J0tJmQEB8Pc+fCyZNmBJiIiPwrjfsSEbEHFxfw9cWWkkKv77/H5urKr1u3snP3bnMM77JlMGUKxMTA32fdiojIHVOwFRG5265eBaD0xIk8cvUq1KzJ53XqEDdjBhw5AmPHmuvmzrVjkSIizsfN3gWIiDidSpXM50WLePqrr9iyZQtnz55l6qhR9IyNNau2AIsXw5UrZiVXRETumHps1WMrIhmhTBlzKllYGLsiI3mzc2fYu5cRXl5UtNnM3NvYWAgKgnXrTO+tiIjcknpsRUTsqV078zk4mMojR9Js717w92d0Sgox3t4wZ455PikJWrTQ+C8RkbtAwVZEJCM8/LD5XLs2zJ3Ls0BgTAxnAwMZ17071q5dkCMHTJ8O+/fDkiV2LVdExBko2IqIZITataFyZbNZLDkZrzFjeG3PHlwrV2bDxo2sGjECHnsMjh6FggVh4sTrx/KKiEj6KNiKiGQEmw1mzYIzZ8zXy5dTZtUquly+DBs2MCE+nqPffw8vvACnT8PChVClCuzZY8+qRUSyNAVbEZGMUqEChIRArlywZg28+ipPnDpF1dy5SUhO5r0HHuDy1q3g6mqO47XZoGFDOHbMzoWLiGRNCrYiIhkpKAiGDYOEBFiwANvEibx24QKFmzThfN68DO/YkURPT3j/ffjlF3Bzg48/tnfVIiJZkoKtiEhG69MHWraEVq3g6afxCQhgaKVKeK9bx4HDhxnXvj1WQgJ8842Zafv11zByJJw7Z+/KRUSyFM2x1RxbEckMSUlmg9jgwRAZCd7e7KxXj2GxsaTExfHkzp10AShbFnbvBg8P8/Hjj9C0qb2rFxGxqyw7x3b+/PkEBwdTt25d6tevz759+257bXx8PP369aNy5crUr1+fBx54gPnz52ditSIiqeTmZjaKPfMMFCgAly5RZckS+nTvDtu2Mcffn4WTJkHdumZKwsmT5p8ffxyOH7d39SIiWYJDBdutW7fStWtXZs2axfr163n22Wdp3rw50dHRt7z+/fffZ8GCBaxbt461a9cyYcIEOnTowK5duzK5chGRVOre3UxKGD8egGb79tEpZ06oVo2vJ03ih0mT4NlnISDAHOLg4QETJti5aBGRrMGhgu3IkSNp2bIlpUuXBqBz584kJSUxZcqUW16/c+dOgoOD8ff3B+D+++/H39+fX375JbNKFhFJm/vug7594ZVXoHdv+OEH2j/8ME/myQO//cYUV1e+zZ+flJQUiIgwq7effmqCbqNGMHcupKTY+y5ERBySQwXb1atXU7169etfu7i4UK1aNVatWnXL69u0acP69es5ceIEAMuXL+fcuXMUKFAgU+oVEUmXMWPM5rAff4QjR7DNm0eX77/nmeBgePBB5q9YwXvPPENM5cqmDcHXF/r1g+RkePJJaN0azp61912IiDgchwm2Fy5cICoq6h+hNDAwkLCwsFu+plu3bgwdOpRKlSpRvnx5WrRoQdu2bXnyySdv+3Pi4+OJioq66UNEJFO5uMDAgXDiBDRvblZlT5zgiQ0beO3NN/FwcWH7rFkMyJuXY7lzm2kKzZuDl5d5/c8/Q2Cg6b/9l30IIiLZjcME29jYWAA8PT1vetzT0/P6c/9v4sSJjBw5kpCQEPbv309oaCg1a9bExeX2tzVixAj8/f2vfwQFBd29mxARSQsPD3jnHdNyMGECWBb16tXjw2rVyJuczJ/u7vQ7eZL5OXNi1a1rNpRNmgT16pkwvH07BAfD7Nn2vhMREYfgMMHW66+ViPj4+Jsej4+Pv/7c31mWxcCBA+nZsyclS5YEoHLlyixZsoQPPvjgtj9nyJAhREZGXv8IDw+/i3chIpJGDzwAH3wA774L1avDhx9S6qef+CxHDmqEhZFUrhzfTp/OoLx5OTRrlpmqULgwnDplgu7Vq9Cxowm4ISH2vhsREbtymGCbN29e/P39OXPtXPW/nD59mhIlSvzj+nPnznHp0iWKFy9+0+P33HMPP/zww21/jqenJ35+fjd9iIjY1ZAhsGKFaS94/33YvRt/d3feXLGCvs89R46YGPYXLcqrQ4Yw6uGHOffdd+Z127fD1Knmn2NioEED2LPHbrchImJvDhNsARo1akTI31YcLMsiNDSUJk2a/OPagIAAPD09iYiIuOnxiIiIW67wiog4tKZNYfFiiI6G6dMhKgpboUI0y52b8UDD1q0hPp5fV6ygZ86cfFuqFNFlysBDD5nXv/WWWckdOvTm75uQYCYpDB8O48aZVV4RESflUMF28ODBLF68mMOHDwMwc+ZMXF1d6dq1KwB16tThjTfeAMzEhK5duzJx4kQuXboEQGhoKCtXrvzXzWMiIg7viSegWDHTYpCQQADwart2jKpWjfuAxKtXmZ8rF88//zzfjhtHOJhDH155BRYuhPPnzfdZuBCKFjWTFMaMgf79zfft3du0MBw5AseOaXyYiDgNN3sX8Hc1atRgypQpdOjQgZw5c+Li4sLy5cvx9fUFzAazv/fgjh49mrfffpvGjRvj5eVFdHQ0I0eO5KWXXrLXLYiI3DkPD1iyBJo1gz59wNUVWrSgdHg4wy2L0N69meLiwrFjx5g/eTLzPT0pt2gRzYoUoU5KCjnPnjXTEp54Alq0MKPFypeHqCizSW3IENPCcG1jbqlSZrZuvnzw669gWebUsyefhBw5bq4tOdnUIyLigGyWZVn2LsKeUnv2sIhIpouKMm0Jo0eb1dWgIDNB4exZrMREtvbrx8pZs9hWqRIpRYvC8ePk2LOH4LfeovKCBVRycSFw61Zs7u7m+yUnQ5s2ZiU3JcWE27x5TYvCsmXmmkqVzDiynTtNz++CBabF4ZNPTC0XL5rnLQsKFTInqb38svk+YB5fs8acmhYZCWXKmGuKFr3zfx+WBb/8AitXmnupWRMefRSu3Z+IOK3U5jUFWwVbEXF0lmV6Zz/6CBITwd/frLa6usK773Kxe3d+XbaMlS++yElPTxNOV66EKlXIX7UqlSpVonLlylQ6eJA8L7wAP/wAXbvCm2+a43vvvdesEv/5J6xfD3XqwKFD0K2b2YyWM6f5uUlJ5nOZMvDHH+Y1SUkm4K5da+bstm5tgm3JklCkCOzYAVeumFXj115L/7+D48fN3N6dO8339fCAo0dNYP7hBzNR4r/8+adZsV640PylwcMDbDbw8YH69aFXL7N6LSIOR8E2lRRsRSTLOH3ahM3ly80s2w8/NEf0rlsHw4Zh7dvHwZkzCT10iF0DB3KwTh2Sc+W68fqNG/FzdyegfXvyTZ9OQPXqBOTPT745cwhYvpx83buTp3Zt3GbMMNdfumSO8s2dG8qWNT9/3Tqzgnv8uAnA5cvD779DtWom5G7cCLNmmU1tNpsJte+9Z0L5rFmmbzitYmKgShWzyvztt+bebTbYtQt69jQhe9cus6J9O7/9ZtoywNS6dq35HsnJULGiGZ92beNe+/bmOssyG+/efx8OHzYr1ZUqmSOOa9VK+32ISLop2KaSgq2IZCmWZdoCRowwwfOaKlXgq6+gRg3TApA/P3HDhvH7Qw+xa9cudu/ezZHx47GKFTOrnKtWmVAcHm5WZKtVg127sEVHk/vRRwkICCAgIYF848YRkCsXAZcvk2/UKLzatsXd3R0PDw88Zs7E4+WXcf/gA2yvv25qmz37RjD8e82PPmrC8K5dJlCmxcSJ0KMH7N9vAvbfXboEJUqY5z/88Navv3IFihc3AfZaKG3b1nzfr782xxVPm2b+wvD992aVukwZaNfOHHsMJjS7uJh7AHNy3O1+nojcdQq2qaRgKyJZ0tWrsHq1+ZV66dLmV/F/D4xdupiNYNu3m15ZILZUKc4GB3Pu6lXOL1vG+c8+49w773C+YEHOV6vG+Z9/JvFa7yqY0Ltrl+lhTUyEhx++eeNYXJwJyFWq4L5zJx6urri3b49Hjhwm+P7tw/3IETy//x73IUPwCAy88bi7O56enjfC8q0e790bD8Bj9uzrz127zs3NDVvfvmaz3dGjt/539dVXZhLEsWMm2M6aZY4zvrYxrmlT09rxyy8m9LdrZwL0Sy+Zf3erV0OFCubaU6fMXwJOnzbj2QoXNhMnliwxo9WqVTMb/h5/PO0BXkRuK7V5zaGmIoiISCrlzAmPPHL754cPNyuTDzwAAwZAvXp4VatG8XnzKJ6UBN98A889Z1oLtm2D/v2xJk4kavRozrVsyfnz5zk3dy7ndu3ifN68nD99mgu5chFns5GQkEB8fDxWUpL5WZZFIpDo6mqCdlTUP+u5eNF83rTJ9LSmxe+/g7e36YH9PzabDfejR/E4eRKPrl3/EXw9PDzw+PlnPIKC8Jg7F/c5c/AsVw7377678Xzp0riPH4/nmjW416qFx6JFBMydS1HAY9myG6EWTD/xmjVQrpypJyLCPPbcc+DrC4sWmWkUvXubTXkKtyKZSsFWRMQZFS1q+l0HDIBXXzX9r2BWXEuWhPvvN1/37GlWMGvXxlaiBP7PPYe/tzelSpWCzZvNNQ0bmg1aVaqYX8H/Jbl/fxJCQ0lo2JDEAweIj48n8eWXSShalISEBBISEkhMTDSPT5hAwo4dJPTqRcJf4TgxMfH6dX//uP6aa8//8QcJp06R6OND/F/PX/tlo2VZJJw5Q4K3943w/P8uXDAr3L/+atoSTpy40WIAN9oLPv/cjEk7fx5iYrDlzEnhCRMoXrz4TR/5y5TBFhhovk/btubf37XJDIMHm7809Ohhwvi+faa1wcvLrOL26fPvvcAickfUiqBWBBFxdmfPml/D+/mZgNemDYSFmcMaEhPNr9fBHMn77LOml3TuXDPqq0IF8yv+OnXMxIRZs6BVKzMqrGdPeOwxs0r54ovmuUqV4Oefb55/u3u3mYvbrRt89lna69+82aw+f/YZvPQSlmWRnJxswu/ixSS0b0/CmDEktGr1z1CckEDCggUkfvYZ8ePHkzhvHgnbt5Pw0UckJCebID12LAlJSSR07UrC6NHEBwVx6vffifb2NqH+/+TMmZNiCxZQPDGR4lOnUuqBByhdujQuLn+deWRZpkUhIsL0PDdtasLy7Nnm+aVLtflMJI3UY5tKCrYiku0kJZn+0I0bzQpuo0YmhI0ZA6Gh5pr77jPzaTt1gqeegvnzzapjbCy4uZnv4etrJgm0b2+mCaxbZ9ojChc2K5ZFipgwPG2a6Vn99Vczqiw9+vUz9bVrZ2ry9DSrrlOmmN7fH34wdd1KTAzcc4+pYcQIM9qrRw8YO9Z8z4EDTehctw7Gj4fQUKyWLbkUEcHxlSs5FhXFsWPHOHbsGCdOnCDp4kVzrYvL9UkLPj4+VKlShapVq3L/nj0E9O9vnk9KutGOcPKk+cvD8eNQubL5S0OPHlC7dvr+nYhkIwq2qaRgKyLyN1FRZsXRz+9GILMs0xs7darpd71wwYwAq1ABnnnGrD5eu3bXLjMtYN48sxpcsCA8/7xpifjrFMl0sSwzxWDUKDhwwDxWuLDpZX3ttf8+pGHzZhOAExNNaN+yxYT6pCTTh3z6tGktmDDBhM0vvzRtA5Urm2s9PQFIOnaMiDp1CDt5kuP58xPWpw+///47MTExN37W2rUEublRNTqaB3ftonz58tjCwszKbVjYjZPdIiLMGLG+fc1qtPpxRW5LwTaVFGxFRDJAUhLEx5tV3rsZ2CzLHLSQlGR6VW+3SnsrERFmvNfPP5sxYTabmezg6WlWcV980Uw1uPZzWreGn34yoblyZTP1YM8e81z58iZgHz1KclAQhw8fJjQ0lNBNmzj4+edYOXKY1engYAoVLEjT5ctp5OpKnhUrzAr3/febWiZMMAF6wgTT2iEit6Rgm0oKtiIicltTp8K775oe5Wu9swMHwtNPm9aGoKAbxw4DnD3LlQIF2AVse/FFfjt9mrjwcNiyBZfatanWrBlNp00juEED3CZONK9p396cqDZtmtls5u0NzZqZVXERARRsU03BVkRE0mXHDnPC2sWLps3Az88cZXzxojmmeO9e4uLi2NC1KyuXLuX3+vVNT/LateSqU4dHX3qJFi1a4P3NN6aH+O9y5jQruSNGpG1VWsRJpTavuWRiTSIiIs7j/vvNcb6ffmqO5j1zxvQcf/qpWXn95BNyeHjQJCiID/PlY8Lbb9M2LIzcXl5c9vNj2rRpdG/XjmmDBhEFZiPctSkVAwfC6NG3nN0rIrenvwaKiIikl7+/OaHspZduPGZZZsTaa6+ZKQulSsHRoxSuVYuuefPSaf161kdGMnfuXMJ//pm5yckszZmTLnny8JCLCy4FC8KwYeZo5DFjTI/v449DkyZm0oKI3JZaEdSKICIiGWHTJrMpbM8eM00if34z8qxkSQCsuDi2+vgwMzmZsHLloFQpSpYsSZ/HH6f0gAEQEmK+j6en2YhXsiQsXGg2rolkM+qxTSUFWxERyXChoWZDWFISdOxoxqCtXAkbNpD8wAMsHTKEGd99R0xkJC7r1tHWy4uOTZrgNneuaXO4xtPTBOZrJ8eJZBPqsRUREXEUVauaGb99+sDq1aZFwcUFPDxwbd6cRx57jAkTJlAvZ05SYmOZ4+ZGv++/56SrqxlDdvq0aUtISDAHapw9a+87EnFICrYiIiKZoXBhGD7cbDiLiIC1a6FrVzPP9uxZcuXKxWsXLzK4UiX8Tp/mWIECvJqQwPbGjaFAAdPH27SpOUTj7bftfTciDknBVkRExF7efNNsNqtdG2bMgAsXqB0byzhvbyqcPk1siRK8++23zBsyBOv++2HFCkhJMSu+LVrA/v32vgMRh6IeW/XYioiIPR0+bI4GXrnyxmPu7iQNG8ZX/v4smzwZQkNpWbYsPd3csMXGmqN5y5SBc+fgt9+0oUycnnpsRUREsoJSpcxK7KFD8M475jE/P9yGDKHPc8/xwqFD2AoVYrGfH2P37cOqUAHy5IEtWyBfPhgwwL71izgQBVsRERFHUKoUDB1qWgwuXDCbxN5/n5bR0fQrXx6X7dtZFRDAlDVroEoVaN0arl6FJUvgu+9MS4NINqdgKyIi4ihsNvj5Z6hUyWwuGz4cgIZ79vBK/foQE8OPMTEs+uUX8PaGWrXM6556Crp1u3k0mEg2pGArIiLiSFxdYds2MxrM7a8DQs+epeGaNXRJSYEcOfi6YUM2v/kmdOpknv/wQ7P5bNQo+9Ut4gC0eUybx0RExFEdPAj33gutWsFjj2E98wxf9unDsuPH8XB3Z2REBKXj480pZT16wLJlZmPZtUAs4iS0eUxERCSrK1sWXnsNfvoJpkzBlicPL4weTfA995CweTOfbt5Mwj33mH7ckBD480+zqUwkm1KwFRERcWTDh8OQIbBhA1y8iGuBArz6+efkOXeOk8CMpUshVy6IizPXN28O69fbs2IRu1GwFRERcWQuLibczp5tvn7ySXw+/JAXU1KgcGEW5MvH/iVLbhzWEBMDjRvr8AbJlhRsRUREsoInnoAKFczKbUgIwUWL0tjHB+vPPxlTvDgJbm7Qvz989BEkJkLDhhAdbe+qRTKVgq2IiEhW4OICP/wAly7BnDlgWTx/8CB5/Pw49ccfLKlSBUaMMD25TZrA2bMwaZK9qxbJVAq2IiIiWUW5crBzp5lhe/o03u7udC5WDKpWZU6pUsQmJprr8uWDgACYMsWe1YpkOgVbERGRrCRfPtNm4OoKnTrRKCSEIjVqEH3lCvPnz4fYWDP2q2xZOHnS3tWKZCoFWxERkaymb18TYFevxtXFhS5dugCwYP58Il94AaKiIE8eCAy0c6EimUvBVkREJKtp1gzatYPwcChbllqrV1P63Dnili5lzowZZorC8uXQtau9KxXJVAq2IiIiWdGsWVCpEhw9im3kSDr9Nd7rV5uN5MGDISXFjP66csXOhYpkHgVbERGRrMjNDX77DTp3hoQEqkRF4RsbS3RKCvtLlYLWrc2UhPr1ITLS3tWKZAoFWxERkazKxwe+/RaKFsX1nnuo3rYt1K/P1rZtoUcPWLwYjh6FwYPtXalIplCwFRERycpWrzbhdepUagQEwKZNbB050syyfeghKFwYpk41G8pEnJyCrYiISFYWEgK5csHcuVSdMAG3woU5WacOJzdsgNGj4cwZuHoVtmyxd6UiGU7BVkREJCvz8DDB9fPP8RozhoqdOkGuXGw9exZefBHGjjXXzZ5t3zpFMoGCrYiISFb28MMQHw/+/tCrFw888AAAW7duNc+vXGl6cRcvtmORIplDwVZERCQrK18eChY0o702bKBGcDAA+/bsIfrDD2HyZHj0UdOSEB9v52JFMpabvQsQERGRO/Tww2aubePG5K9UiYBLlzh/9iynFi+m7CuvgLc3+PqatgURJ6YVWxERkayue3eIi4PXX4dKlfB1cYECBYj94gsYOBAmTYJOncBms3elIhlKwVZERCSre/BBaNXKTEEIDMTb1RVOnCCmTx8z7uvSJXj6aXtXKZLhFGxFRESyOpsNvv8e6tWDTz7B++hRAGIALAuSkkyfbXi4XcsUyWgKtiIiIs7AwwN27wZXV7x8faFwYWJeeQUWLYJateD8eXjySXtXKZKhFGxFREScwbJlEBEBRYvi8/77cP/9xFStCi1bwtq1cP/9sHkznDhh70pFMoyCrYiIiDP47TfzedgwvPLkASAmJsY85uIC775r/nnaNDsUJ5I5FGxFREScwbUZtffei7e3NwCxsbE3nvfyMp8vXMjkwkQyj4KtiIiIM3jkEfP5q6+uB9srV67ceH7MGPP53nszty6RTKQDGkRERJxBgwaQJw9MmoR3rlyQkmJWbC9dgg8+gIULwd0d2rSxd6UiGUbBVkRExBnYbDBxIjzxBN6ffAJubsRs2QJjx95oUxgyBHLntm+dIhlIrQgiIiLOonVr+P57vP38ICmJmIsXzYlk7u4wbBi8/ba9KxTJUFqxFRERcSZPPol3cDC0a0dMUhK8/DI8/rhWaiVbULAVERFxMt558kDBgsQCKV274uKiX9BK9qA/6SIiIk7GKy4OjhyBdeu4WrIkNG8O8+ZBcrK9SxPJUAq2IiIizuTwYdyDg/E4eBB8fIh55BGIjYV27cxEhMREe1cokmEUbEVERJyFZUHbtuDlhXfr1lC1KjGvvgrr15txX0uWwPDh9q5SJMMo2IqIiDiLtWth1y4YPx6vgADgb8fqPvII9OwJ48dDQoIdixTJOAq2IiIizmL9eggIgAYN8PHxAf4WbAGefBLOnoWDB+1UoEjGUrAVERFxFjabaUewLLy8vID/C7YpKTeuE3FCCrYiIiLOokEDuHABVq/G29sb+L9gO3s2FCwIZcvapz6RDKZgKyIi4ixq14bq1eGFF/COjQX+CraWBXPnwjffQJ8+5iQyESekAxpERESchc1mAmzjxni//z7ky0fM1avwyScQEgLt28OgQfauUiTDaMVWRETEmRQvDjt24N21KyQkELNvHxQpAosWwaxZ4KY1LXFe+tMtIiLibPz88G7TBs6dI+bBB2HIEHtXJJIptGIrIiLihK5tHov9q9dWJDtQsBUREXFCt5yKIOLkFGxFRESckIKtZEcKtiIiIk7olgc0iDg5BVsREREn9PcjdS3LsnM1IpnD4YLt/PnzCQ4Opm7dutSvX599+/b96/VHjx6lTZs2NGzYkHvvvZeaNWuyffv2TKpWRETEMV1bsU1KSiIxMdHO1YhkDocKtlu3bqVr167MmjWL9evX8+yzz9K8eXOio6Nvef25c+do3LgxL7/8Mr/++iu7du3Cy8uLw4cPZ3LlIiIijiVnzpzYbDZA7QiSfThUsB05ciQtW7akdOnSAHTu3JmkpCSmTJlyy+s//PBDatWqRb169QBwc3Pj66+/vv61iIhIdmWz2bSBTLIdhwq2q1evpnr16te/dnFxoVq1aqxateqW1//444//CLGlSpWiUKFCGVqniIhIVqANZJLdOEywvXDhAlFRURQoUOCmxwMDAwkLC/vH9TExMYSFhZGcnEynTp2oXbs2zZs3Z+nSpf/6c+Lj44mKirrpQ0RExBn9fQOZSHbgMMH22skonp6eNz3u6el5y1NTLl++DMDQoUMZOHAgv/32GwMHDqRVq1asXLnytj9nxIgR+Pv7X/8ICgq6ezchIiLiQLRiK9mNwwTba//xxcfH3/R4fHz89ef+ztXVFYBWrVpRuXJlABo3bkyjRo347LPPbvtzhgwZQmRk5PWP8PDwu3ULIiIiDkU9tpLduNm7gGvy5s2Lv78/Z86cuenx06dPU6JEiX9cny9fPjw9PSlcuPBNjxcrVoyNGzfe9ud4enr+Y1VYRETEGSnYSnbjMCu2AI0aNSIkJOT615ZlERoaSpMmTf5xraurK7Vr1yYiIuKmx8+cOUPRokUzvFYRERFH521ZcPEisfv2QXKyvcsRyXAOFWwHDx7M4sWLr8+hnTlzJq6urnTt2hWAOnXq8MYbb1y/ftCgQfz000+cOHECgN9//50VK1bQp0+fzC9eRETEUZw5A5064T1wIGzcyJVhw6BECfjyS9ApZOLEHKYVAaBGjRpMmTKFDh06kDNnTlxcXFi+fDm+vr6A2WD29x7cZs2aMXbsWB577DF8fHxISkpi6tSpPPLII/a6BREREfu6cAHq1oWoKLy7dIGwMGLvvReuXIE+feDUKXj/fXtXKZIhbFY2P0A6KioKf39/IiMj8fPzs3c5IiIid2bQIPjqKwgNZeWRI4wdO5bg4GDeeustGD4chg6Fw4fNCq5IFpHavOZQrQgiIiJyB1JS4NtvoXt3KFHi+uaxK1eumOf79YNcucw1Ik5IwVZERMRZxMTA+fNQowZwi6kIXl5w331wi4OPRJyBgq2IiIizyJkTcuSAo0cBCPD1hYgITq9fT9KUKXDpEhw7Bnnz2rVMkYziUJvHRERE5A64ucGTT8LXX0NgIIUGDsT7wgViXF05/swzlPT0hPh4eOope1cqkiG0YisiIuJMBg82476efRZblSqUfuEFaNqUP55++sYs23Xr7FujSAbRiq2IiIgzKVcOChSAc+dg9WrK/vYbO+PjOQg83Lkz+PqacV+9e4OPj72rFbmrFGxFREScSWgoHD8OK1YAUGbRIvjlF/6oUgWmTYPwcHNQw6JF0KGDfWsVucsUbEVERJzJhQvmc5kyUKwYZYKD4ehR/oyMJDY2Fq+gIPDwMNMTRJyMemxFREScSbFi5vO2bQDkypWL/PnzY1mWObJ+925ISIDixe1Xo0gGUbAVERFxJmXLQu3a8MEHEBsLQJkyZQD448ABePttKFQImje3Y5EiGUPBVkRExNmMGQMHD5qAO3s2ZXx84OxZ/nj9dViwAD7/HNzd7V2lyF2nHlsRERFnU706rF0LAwZAx46U/uvhg7lzm01jLVrYtTyRjKIVWxEREWdUvTqsWQOHD1Nq2TJsDRpw8cEHueDlBRs3avOYOCUFWxEREWdWsiQ5mjWjGMAvv3CwYUPTolCoEHTuDKdP27tCkbtGwVZERMTZvfce965ZA7lzs/qZZ2DPHhg5Elatgrp1tXorTuOOg+3Vq1c5efLkPx7ft2/fnX5rERERuVPHj8M779DqlVewVavG1nPnOO7rC6++Cps2mVA7cqS9qxS5K+4o2M6bN4/SpUvTsmVLKlWqxJYtW64/16VLlzsuTkRERO7QlCng40Ph99/nwQcfhIQE5vXpA/XqmZPHihaFb76B5GR7Vypyx+4o2L7//vuEhISwc+dOJk+ezLPPPsusWbMAsCzrrhQoIiIid+DoUahYEby9aVe+PKxZw9olSzjt62seP3kSoqLgnXfsXanIHUt1sB04cCBxcXE3PZaYmEiBAgUAqFatGuvWreOrr77i3XffxWaz3d1KRUREJO3y5jXtCNHRlOzVi2oBAVhNmjDv4Ydh0iTo3x9cXOC998woMJEsLNXBdsyYMURGRgLQrVs3YmJiyJ8/P7t3775+TZ48eVi5ciX79++/6XERERGxk44dzarsa6/B6dM8OWkSeHqyYsUK9m7dChMnQuvWUKkSvP8+6DeukoWlOtgWKlSInTt3AjB9+nRiYmKYPn06+fPnv+k6Dw8PvvvuO9auXXtXCxUREZF0CA6Gxx83AbZ4cSrUrUuzpk2xLl5k9COPEHv8OCxcCLt3w5YtJuB+/729qxZJl1QH2/79+9OqVSvq1q0LwMyZMzl16hT+/v63vL527dp3p0IRERG5M7NmQZEiEBYGuXPz3NCh5P/tN86eP883np5mpfb11821RYuaTWXjxtm3ZpF0SHWw7du3L9u3b+ehhx7Csiy++OILHnzwQfz8/ChfvjwdOnRg5MiRLF26NCPrFRERkbTKmdOM93Jzg2HDyPnmm/SrVw+bmxuratZkftGi8NNP4OsLR46YENyvn2lhEMlCbFY6xheULl2aTZs24e3tze7du9m5c+f1j7179xIdHZ0RtWaIqKgo/P39iYyMxM/Pz97liIiIZIzLl6FYMahVy6zgFivGD82bM+XKFVi7llfi4mhcqRI0bgzbt8P69VCiBOzaBT4+9q5esrnU5rV0Bdt/Y1lWlpqIoGArIiLZxqpV8Nhj4OkJly5hdejA5J9+Yv7Vq7gUKsRrs2dT56+WQ0qXNtMUunY1c25F7Ci1ee2uH6mblUKtiIhIttKkiVmBbd8eANvixTxz9SqN69YlpWpVPvr4Y1avXg2JiRAZaY7bnT5dR+5KlnHXV2yzGq3YiohIttSyJezZA+HhpFy+zLipU1m5ciUAnYKCaD9+PLZly+Chh+C778Df34wCCw6GfPnsXLxkN6nNa26ZWJOIiIg4iqFDoU4dAFwOHaJv3754u7uzYPx4Zi5ZwuGKFemXPz/eAE8/bVZxATw8oFMnGDMGtCAkDuautyKIiIhIFlCzJnz9tfnn4GBshQrx7IABvLRnD+6FC7OlSBH6NWjAATCHOxw5AseOwfDh8MMP0Lw5xMfb8QZE/knBVkREJLvq3h1atTIrr61bw3vv0fTIET5cvZp8Z88SERXFwHz5mFqmDAlFipipCgMGwLJlsHmzCcfVq0OLFqZdISHB3nck2Zx6bNVjKyIi2dn582ZT2Z49JqCWLw+7dnFlxQq+9vTk14YNwc2NwMBAnnvuOWpUqoStTRtYutSMAXvqKTh4ENauhQceMKE3Vy5735U4GbuN+8pqFGxFRCTbi42FGTNg2jQ4c8Yc0HD4MLRqxaYuXZgwYQIXL14EoOqZM3TdtYsSjz9ujuA9dgwuXoTBg2HSJPP9ihY1fbl9+0JAgN1uS5yHgm0qKdiKiIjcQpMmYLPBypXExcXx/fffs2DuXJKWLoVSpajj7k4nd3eKLFkC9eubU8qqVoUNG6BjR/j5Z8if36zkBgXZ+24ki7PbHFsRERFxAl26mAMdtm8nR44cdO3alS87daJ+Sgq23LnZsG8fvZOSGNGoEQcjI00rw9Kl5rXNmsG+fZCUBI8+ag55aNsW3noLTpyw732JU1OwFRERkX/q0MFsDHvoIZg4Ea5coaCXFwOAsX/8wQP582PlzcvGAwcYULAgg7/+mq07dmABpKSYsWAuLrBzJ2zdClFRZkTYPfeY7z1vHsTE2PUWxfko2IqIiMg/eXrCihWmzaBHD/D1hccfB6B4gQK8uWcPX3bsSBPArXBh9u3bx3svv8xzwJzLl7nYsuWNKQlDh8JLL5lNZSkp8P330K4dFC4Mn35qDn4QuQsUbEVEROTWcuc2M2uPHIEpU2DyZDMW7PRpCA8nqGBBXgYmDh/OE7Vr433gAGcDApg+dy7P7NjBBw0aEAIk791rQnGlSrBtG9SrB9WqwcMPm/Fh3t5mwkLdujB7toKupJs2j2nzmIiISOpFRkLTphASYnppV62CcuXg4EESihfnt+HDWfrhh+z//Xcz9/bgQfz9/amXLx/1FyygTPny2KZNg27dwM0NvLwgLs703/7yi/l45hnT/mBZ5ud5e5sVZMm2tHlMRERE7j5/fzPp4MsvzZgvDw+zUeypp/DYupWG7drxUXAwX+TNS6vDh/ELCiLy8mUW5srFgEGD6NmzJzM3bSIczIrtrl3muN6iRWH1apg+3awMt2oFgYGQN68Jtm3bQmiove9eHJyCrYiIiKRNzpzQs6eZY3vpkmkzmDoVatUyvbM//UTRP/+kR716TP3qK4YB9Rs0wNPTk4iICGZ//z29gd558zJr40aOe3lhnTljvvfDD5u2hGXLoHNnmDsXPv7YhOcHH4SVK+144+LoFGxFREQk/Tw8TB/ur79CjRpw+bLZcPZX36xb8eJUBwZUq8aMGTPo37Qp1SMjcfPyIvz8eb6bOJEXY2LovWIFM2bM4OhLL2ElJ5s2hE8/NSu1/fqZ6QoNG5oZuZ07m5VjNzeoXBnGjzervpLtqcdWPbYiIiJ33+LF8MQTps82KQmSk82K7o8/Qu7cXClWjK0jRrChf392/P47SU2bmokJK1cSGBjIgxERPLhvH2XKlsVms5nv+dVX8MILkC8fvPii+bxqFSxYYMaSzZ9vgrY4HZ08lkoKtiIiIhkkNBQ++cTMrE1MNBvAOnaE8uVh0CBo3BhWryZmwgS2lS3Lxh9/JOTzz0nw9jYrvjVqEBAQwAMPPEDNSpWo+PDDuCUkmBXcDz648XNWroSWLeH992HgQPvdr2QYBdtUUrAVERHJYJYFGzea0V6bN9943MPDBN++fc3Xe/cSd999bAc29ujBtosXiYuLM88dP47Xnj1U8/CgZteuVBs9Gm9v7xvf65lnYM0aM5rM5a9Oy4QEs5q7YAHExkLFivDcc1C8eIbfstxdCrappGArIiKSiX7/HcLCzElkAwaYmbgNG5rDG1avNj26990Hu3aRkJjIrl272Lx5M1vHjuXyyZNw9So0aICrvz8VK1akZs2a1KxZk4C1a00/7rlzEBAA4eGmPeH33yE42Dz2229w5Qp89plpZZAsQ8E2lRRsRURE7OTKFZg5ExYtgvh4uP9+KFQIXnkF+vc3J5b5+0NKClbLlvyxfDlbqlZlc40ahIeH3/StSickUHPFCmru3UtQ2bLYqlUzM3DnzzffF8wRvm++aY72XbTItC9IlqBgm0oKtiIiIg5m1CjTg+vpaU4r+/NPswIL5pjfpk2JiIhg8+bNbN68mf3792Nt3Gg2n9WuTcHERGotX06tWbMo26HDjc1nYNoi6tY1ExXWrIEzZ8yhEAkJUL063HuvXW5Z/p2CbSop2IqIiDigU6fMQQ2HD5tV23btoFcviI42PbOVK5vrYmO5PHgwWz//nM0dO7Lj6lWSduyA8+ehYUPy5MnDAw88QK1atbjvvvtwc3ODSZNMr23XrjBr1s2jwooVM6vFPXrotDMHomCbSgq2IiIiWcSJE6Zvdv9+eOAByJ8fNmwwfbmffAKvvsrVq1cJadeOTbt2sf3BB4mNjb3+cl9fX2rXrk3d2FgqDhyIS86cpt1h+3YzhszT88ZosoAAmDbNHBghdqdgm0oKtiIiIllIQoLpm/3xxxuTDp5/HkqUuHHNl1/CSy+RePgwuy9cuN6ycPnyZfP85s3kPn+eOr16UffQIcr99hu2sWPNwQ8XLkC5clCggGl/WLsWata0y63KDQq2qaRgKyIi4mSioqBIETMn9/vvwcODlJQU9u7dy7qvv2bjF18Q7eFhVn3Xr6dA/fo0692bxo0bkzdvXjMx4aefIHdu05qwcKHpzT10yGx4K14c8uSx911mKwq2qaRgKyIi4oQWLjTjv4KCzIzbfPnMxrMFC0jKlYtd993HOhcXNv32G1ebNAGbDZvNRnBwMM2ioqg+Zgyu18aCTZ1qWh127zbf28MDnnwSPvzQTHGQDKdgm0oKtiIiIk5qxw4TSOfPh7g4M/HghRdMT+2GDdC4MfGbNrFxzBiWL1/Ovn37zOt27ybPxYs069mT5p9+SgCYXtvevaFgQTNN4dNPTU/upk0QGGjHm8weFGxTScFWREQkG0hJuXEi2YYNZuTXU0/BDz/AyZOQNy9//vknK2bM4Jc33ySyZElITMQlLIwa1avzyDffUKly5Rujw8LDzXiwxx+Hr76y221lF6nNay6ZWJOIiIiIfbj8LfLUrg3t28PcuWbUV79+cOoURZYsofvnnzOlRAkGDhxIxfBwUoDNuXPz5tChvPTSS/zyyy8kJSWZFofevc0BE1ev2u225GZasdWKrYiISPaTmAhvvAFjx5pTz8CE37p14cEHYeJESEzkRO7cLBk4kNWrVxMXFwdAnjx5eOyxx3jUywu3hx82RwQXL26/e8kG1IqQSgq2IiIi2Vh0NIwYYQ5qOH7cPJYjh2lTyJPHHOYQEUF0QgLLli1j0aJFXLx4EYBXypSh8ahRZkSYpiRkKAXbVFKwFRERESwLjh69Mc7L3x9+/91sOJs4EZ59FoDExESGDx9OyLZtPLN/P0+UKQPLltm39mwgtXnNLRNrEhEREXFMNhuULHnzYxUqmJXbPn3MVIVu3XD39iZfcjJs307ihQswfbp96pVbUrAVERERuZ1vvzVza/v2hddeA39/3E+fBg8PEl5+2WxEE4ehYCsiIiJyO56eMHkyvPUWLFgA0dG4R0TAiRMk3nuvvauT/6NgKyIiIvJf7rnHjAUDPGbOhNmzSUhIsHNR8v80x1ZEREQkDdzd3QGzkUwci4KtiIiISBoo2DouBVsRERGRNPDw8ABQK4IDUrAVERERSQMFW8elYCsiIiKSBmpFcFwKtiIiIiJpoGDruBRsRURERNJArQiOS8FWREREJA20Yuu4FGxFRERE0kArto5LwVZEREQkDbRi67gUbEVERETS4Fqw1Yqt43G4YDt//nyCg4OpW7cu9evXZ9++fal63bhx47DZbKxZsyZjCxQREZFs7VorglZsHY+bvQv4u61bt9K1a1dCQkIoXbo006ZNo3nz5uzfvx9fX9/bvu7UqVN8/PHHmVipiIiIZFfqsXVcDrViO3LkSFq2bEnp0qUB6Ny5M0lJSUyZMuVfX9e3b19ef/31TKhQREREsrtrrQhJSUlYlmXnauTvHCrYrl69murVq1//2sXFhWrVqrFq1arbvmbhwoW4u7vTvHnzVP2M+Ph4oqKibvoQERERSa1rwRbUjuBoHCbYXrhwgaioKAoUKHDT44GBgYSFhd3yNTExMbzxxhuMHj061T9nxIgR+Pv7X/8ICgq6o7pFREQke7nWigAKto7GYYJtbGwsAJ6enjc97unpef25/zd06FBeeOEFChYsmOqfM2TIECIjI69/hIeHp79oERERyXZcXV2x2WyA+mwdjcNsHvPy8gJMq8DfxcfHX3/u70JDQ9myZQuffPJJmn6Op6fnP8KziIiISGrZbDbc3d1JSEjQiq2DcZhgmzdvXvz9/Tlz5sxNj58+fZoSJUr84/rFixdz9epVGjVqBEBcXBwAr7zyCrly5WLixImUKlUq4wsXERGRbEfB1jE5TLAFaNSoESEhIde/tiyL0NBQ3njjjX9cO3ToUIYOHXr962PHjnHPPfcwZswYGjRokBnlioiISDbl4eFBTEyMWhEcjMP02AIMHjyYxYsXc/jwYQBmzpyJq6srXbt2BaBOnTq3DLkiIiIimUmnjzkmh1qxrVGjBlOmTKFDhw7kzJkTFxcXli9ffv1whtjY2H/04IJpP9i8efP1fy5XrhyzZ8/O1NpFREQk+9DpY47JoYItQOvWrWnduvUtnwsNDb3l42PGjMnAikRERERudm3FVsHWsThUK4KIiIhIVqBjdR2Tgq2IiIhIGmnF1jEp2IqIiIikkVZsHZOCrYiIiEgaacXWMSnYioiIiKSRxn05JgVbERERkTTSuC/HpGArIiIikkbqsXVMCrYiIiIiaaQeW8ekYCsiIiKSRgq2jknBVkRERCSN1IrgmBRsRURERNJIUxEck4KtiIiISBppKoJjUrAVERERSSP12DomBVsRERGRNFKPrWNSsBURERFJI63YOiYFWxEREZE00oqtY1KwFREREUkjrdg6JgVbERERkTRSsHVMCrYiIiIiaaRWBMekYCsiIiKSRjqgwTEp2IqIiIikkVoRHJOCrYiIiEga6eQxx6RgKyIiIpJG6rF1TAq2IiIiIml0rRUhKSkJy7LsXI1co2ArIiIikkbXgi2oHcGRKNiKiIiIpNG1VgRQsHUkCrYiIiIiaeTq6orNZgPUZ+tIFGxFRERE0shms2nklwNSsBURERFJBx3S4HgUbEVERETSQbNsHY+CrYiIiEg6aMXW8SjYioiIiKSDVmwdj4KtiIiISDro9DHHo2ArIiIikg6aiuB4FGxFRERE0kHB1vEo2IqIiIikg1oRHI+CrYiIiEg6aCqC41GwFREREUkHtSI4HgVbERERkXTQuC/Ho2ArIiIikg7qsXU8CrYiIiIi6aBWBMejYCsiIiKSDto85ngUbEVERETSQT22jkfBVkRERCQdtGLreBRsRURERNJBK7aOR8FWREREJB20Yut4FGxFRERE0kErto5HwVZEREQkHTTuy/Eo2IqIiIikgw5ocDwKtiIiIiLpoBVbx6NgKyIiIpIOCraOR8FWREREJB3UiuB4FGxFRERE0kHjvhyPgq2IiIhIOmjcl+NRsBURERFJB/XYOh4FWxEREZF0UI+t41GwFREREUmHayu2SUlJWJZl52oEFGxFRERE0uXaii2oHcFRKNiKiIiIpMO1FVtQO4KjULAVERERSQdXV1dsNhugFVtHoWArIiIikg42m02TERyMgq2IiIhIOmkygmNRsBURERFJJ50+5lgUbEVERETSSa0IjkXBVkRERCSddKyuY1GwFREREUkn9dg6FgVbERERkXRSK4JjUbAVERERSScFW8eiYCsiIiKSTmpFcCwKtiIiIiLppHFfjkXBVkRERCSd1IrgWBRsRURERNJJrQiOxeGC7fz58wkODqZu3brUr1+fffv23fbaOXPm0KxZMxo3bkxwcDDt2rXj2LFjmVesiIiIZGuaY+tYHCrYbt26la5duzJr1izWr1/Ps88+S/PmzYmOjr7l9Z07d6Z///6sXr2aLVu2kDNnTh566CHi4+MzuXIRERHJjtSK4FgcKtiOHDmSli1bUrp0acAE16SkJKZMmXLL6x977DGaN28OgIuLCy+99BIHDx4kNDQ0s0oWERGRbEytCI7FoYLt6tWrqV69+vWvXVxcqFatGqtWrbrl9XPnzr3p6xw5cgBoxVZEREQyhaYiOBY3exdwzYULF4iKiqJAgQI3PR4YGMi2bdtS9T02bdpEoUKFqF279m2viY+Pvyn4RkVFpa9gERERyfbUiuBYHGbFNjY2FgBPT8+bHvf09Lz+3L+Jj4/n448/Zty4cdf/kN3KiBEj8Pf3v/4RFBR0Z4WLiIhItqXNY47FYYKtl5cX8M82gvj4+OvP/ZuePXvSvn17Wrdu/a/XDRkyhMjIyOsf4eHh6S9aREREsjX12DoWh2lFyJs3L/7+/pw5c+amx0+fPk2JEiX+9bWDBw/Gy8uL99577z9/jqen5z9WhUVERETSJDkZ5szBfcQIOHCAxPXr4dw5eOklKF7c3tVlWw6zYgvQqFEjQkJCrn9tWRahoaE0adLktq8ZOXIk4eHhjBs3DoCQkJCbvoeIiIjIXZWUBB07wlNP4e7mBiVKkFimDEybBpUrw8aN9q4w23KoYDt48GAWL17M4cOHAZg5cyaurq507doVgDp16vDGG29cv37ChAnMmDGDvn37Ehoayvbt21m4cCF79uyxS/0iIiKSDYwdCz/+CPPn4zFuHJQuTULDhnD0qAm2bdqAJjTZhcO0IgDUqFGDKVOm0KFDB3LmzImLiwvLly/H19cXMBvMrvXgRkdH06dPH1JSUqhVq9ZN32fy5MmZXruIiIhkAykp8MUX8NRT8PjjuP81uSkxMRH8/OCbb6BcOZg3Dzp1snOx2Y9DBVuA1q1b33YD2N8PXvD19SU5OTmzyhIRERGBs2fNyuwnnwA3xn1d3/xetizcey9s2qRgawcO1YogIiIi4tBcXc3nv6YgXJu/Hx4efmMDfHz8jeskUynYioiIiKRWQABUqQIzZgBQsGBBqlSsiHXiBD/Xq2cmIhw+bIKtRoBlOgVbERERkdSy2eDVV2HRIvjoIzh/ntY//QS7drHi4kViIiPBywtGj4aGDUEnnGYqBVsRERGRtOjcGYYMgUGDoFgx7j9yhKIFChB3/jzL3N1h1y7TY7t3L/Tta+9qsxUFWxEREZG0sNnggw9g4UK4ehVbkSI8XqQI3HcfC5s2JalYMcidG557DmbNgv87fEoyjsNNRRARERHJEq5cAcuCbdto4OvLtO7dubBvH2tKlaLJiRM3ruvQAZ54ApYuhchIKF0ann8eHnzQhGS5a7RiKyIiIpIelmU+22y4u7vzmM0GO3YwKTmZ87NmweLF5vl168xRu1eumFD7229Qpw706XPje8hdoWArIiIikh61a4OLC3z/PZw6xeOzZlG6WjWu3H8/Y/btwzpwwKzI5ssHPj4QHAxTpsDBgzBhAowfD59/bu+7cCoKtiIiIiLpUbSoaTF4800YMQI3T0/6T5+Oh4cHu9au5efXXzcrslOmQM+eMHkyJCWZMNyzJzz9tJmeoAOn7hoFWxEREZH0+uorKFYMxo0Dd3cKf/stzx07Bhs2MMVm43d/f2jWzKzuXroEly/feG2nTnDsmDnJTO4KBVsRERGR9MqTBzZsgKZN4epVmD+fh3x8qN22LUmFCzM8Pp7TZ86Y8OriYg52mDPH9Nt6eprvkZRk33twIjbLyt5dy1FRUfj7+xMZGYmfn5+9yxEREZGsaNs2qFHD9Ns++STx8fEMbt+ewz/9RGC1aozYuZOA5GRwd4fERPDzg8qV4cAB+PNP8PCw9x04tNTmNa3YioiIiNyp4GB49FHo3h0mTcLTshj6zTcE5sjB6ZAQXk9O5sLXX5tjdo8dg0aNYP16qFRJofYu0oqtVmxFRETkboiJgWefNau23t7g68u506cZApxxdaVw2bJ88MQT5Nm2DZYvN0fvxsfD669Dr15QsKC978BhpTavKdgq2IqIiMjddOgQ/PQThIXBl19ydswYhnz7LWcPH6ZwUhIf2GzkiY83q7W7d99YsZ04Ebp0sW/tDkqtCCIiIiL2ULo0DBgAbdsCkL9lSz5YsYKAtm056e7OEE9PLqxbB7t2mVXbQYNMG0O3bmYjmqSbgq2IiIhIRggKMp9DQylQoAAjH3yQ/DExnKpShSHTpxMxZw7ExsKIETBvHqSkQJs2sHmzeZ1lQXi46cnV5IRUUbAVERERyQilSkHdujByJFy9SoENGxhRqRIFSpcm4vBhXuvYkYM2G7z7LqxZA08+CWfPQr160K8fVKhgDoG45x4oXhyGDzcTFeS2FGxFREREMsqnn5ojdOvWhSNHyO/jw0ePPELJLVuITEnh9cqV2VC3LtSvD82bm9cULAhjxphgu2ABLF0KLVvCO++YFV2t3t6WNo9p85iIiIhkpK1bzQrsxo3XH4oDPqpenW2BgQB07tyZJ7//Htvu3XDkiLlo0SITaK9ZuhRatICpU81xvNmINo+JiIiIOIIaNeC338zcWnd3qFWLHMCb06bx2GOPATDj00/5aMEC4vLlM20HPj5mpffvHn7YHM/71VeZfgtZhYKtiIiISGaoUwcmT4YtWwBw+ewzngNevHQJt23b2FCgAK8dP86ZypXNprJrK5MRESYU79hhWhYOHLDfPTg4tSKoFUFEREQy04YN8NBDJrxaFlSsyO9PPMGIw4e5vHIlvlevMjAujiqbNpkNYz//bCYmAPj7m1Xfc+fsew+ZTK0IIiIiIo6oTh2YMMGE2j594JdfqPDOO4weMYLSefIQfeUKbwUEMK9ZM6zQUBg3DvbvNwE3NhbOn4dRo+x9Fw5JwVZEREQks3XuDJ99BpMmQZEiULYsARUrMvLgQZp6emKdO8fUK1d4v317ojt3NkfvfvCB6b3t3h2GDMl2q7apoVYEtSKIiIiIvVy8CLNmmeN3AwKgQweIi2NZhQp8DSTmyEE+NzcGXblC2aAg+OEHc12ZMmZCQpcu0KoVeHra+04yVGrzmoKtgq2IiIg4krAwKFGCowMHMnL5ciKio3HNm5dur73GY3/8ge2DD0xLgru7ObAhIMDMve3Uyd6VZxj12IqIiIhkRblygc1GiRIlGLNxI3V69CC5QAEmvfUWw998kyvduoGvLzRqZE4lO3/etDY0bAh79ti7ertSsBURERFxJLlzmzaDcePwcnVl4MCB9OraFbdDh9hSogQvh4RwMDoaVq2CmjXNRrRSpcys3Bo1zOPZlIKtiIiIiKMZNgwOHYKWLbGFhNAiPp6Pk5MJ9PXl7JYtDALmv/MO1syZ0LMnfPmlaUu4/37Tp3v1qr3vwC4UbEVEREQcTXAwLFliwm1wMHTvTilgzN691HFzI7loUb7dvJl33nmHyMhIKFnSvO6FF+DCBZgzx67l24uCrYiIiIgjatQIjh6FRYuubwzznjmTgUlJ9Hn2WTw8PAgJCeHll19m348/mtc8+CCULw+hoXYs3H4UbEVEREQclasrtGwJX30FgYEwbRo24KEyZfj0008pUqQIF86e5fX33mNu+fJYJUpAdLTTj/+6HQVbEREREUfn7m5OIFu61ExE+OgjisfHM7pmTRru3ElKTAzTAgJ4++mnifzzTxOGN26E3r2hXTt49dVsMTFBc2w1x1ZERESyiiVLTFg9fvz6Q1bDhqxq25YJc+eSsGkTeXx8GFS5MhV++QWKFzcTE/bsgTNnoHp1aNoUateGhx4yK8JZgObYioiIiDibFi3MAQ6vvmq+9vPDljMnTSdOZNSaNRTx8eFijhy8vmYNPw8YgHX4sDm2t3Bhc/327fDFF/DII1CuHOzebb97yQAKtiIiIiJZic0Gn34Khw+b1Vt3d6hQgWLz5zN682bqRUSQXLYs3xw4wCcjRxLXqJE5unflSnjxRXBzg3XrwMcHmjSBiAh739Fdo2ArIiIikhWVLAkjRsCCBTBjBjz+ODl++40BKSk8/+abuLq6sm7OHAYcOcKpqVNNiH3uORNyN2yAbt3gyhWzgusk3OxdgIiIiIjcJfHx2Gw2Hm3fnpKVK/Nh06Ycz5ePV7/8kv45cxK8Zo257vXXb7zmww/NwQ5t2til5LtJK7YiIiIizqJKFbAsWLGCe++9lzEFC1I+MJCYmBjeff55Zg8ciAWmLSEpCZ5/HlxczOSEn36yc/F3TsFWRERExFkEB0PVqjBwIFy4QJ4KFfggLo4WzZrB778z08WF4eXLEwfw+OPwzTeQkAA5c5qQGx9v5xu4Mwq2IiIiIs7CZoNp0+D0aahQAQC3Q4foNWkSLycm4u7hwRZXVwY1bcr5axMRevY0J5adOwcNGpigm0Up2IqIiIg4k3vvNWO9/t5esGsXTYARgwbhv28fR7296f/nnxxt2NAc/DBjhrlu69YsvZlMwVZERETE2RQrZgJrZCTExppVWaDsO+8wyrIompLCxVKlGJQ7N9t27IBdu8zrSpSAd96BsWPN9IQsRieP6eQxERERcXZJSeYUsuRkKFyYmFWrGPnpp+zcuRNbSgovbNpEi0uXIEcOiIszs3FdXeGTT6BPH3tXr5PHREREROQvbm4werTpvf3jD7z37GFY7940DQrCWr2a8ZcuMbt5c6z+/c31+fKZEPzii9C9e5bZVKYVW63YioiISHYxZAiMHHn9SwuYDcwqWhSKFaP1hg084+qKrVcvKFrUnHB2+jTUrQvLloGXl13K1oqtiIiIiNzsvfegYkUoUAA+/BDbwIF0dHPj+ddfh02bmG9ZfNOjB9Znn8GAATBlinndtm0wdKhdS08NBVsRERGR7MLNDZYuNa0GgwbB1KkAPNq3L32TkrCVLs3CEyeYMGEClmVB4cLmdW3awKRJZiOaA1OwFREREclOihSBHTvMKLCKFc3GsmrVaJY3Ly998QU2m40lS5bw5ZdfYv36q9lEVrCgmbCwY4e9q/9X6rFVj62IiIhkV4mJZjSYry9ERMDFi/yybh1jxozBunCBR7ZupUdSErZr1+fMCS+/bFoa3NwyrUz12IqIiIjIv3N3h2+/haNHIToaXn6ZRkA/Ly9sGzeyKDmZmd26QadOpi3hlVfg44+hVy87F35rWrHViq2IiIhkd5s2wSOP3DiUwdWVJS4ujK9ZEy5d4rm9e3ls9GgTbL/6Cl54AfbsMa0MmUArtiIiIiKSOrVqwf79N4JqSgotypShy4EDsHcvk4oXZ3vt2pCSAg89ZDaf/bXxzJEo2IqIiIgI5M8PISGmNcGywLJo16oVzV59Fevee/m4Rw/CS5QwJ5idOwdff20+HOiX/wq2IiIiImJ4eMDTT5vNZE88gW3SJHqNGMG9x44Ru3Mn77m5ET11Kvj7Q2Ag9OxpNpM5SLhVsBURERGRG1xdoXNnsxp79ixuS5YwZN8+8jdoQET58nw2bx5WZCT8+CN8+SV8/jmsX2/vqgFtHtPmMREREZH/Fx4OwcFmZTZHDvD05OiHH9K/Z0+SDh3i5RYtaLJ4sVmpLV8eqlaFWbMyrBxtHhMRERGR9AkKMquwhQrB7t2wbRslGjWiU3g4lCrF115enD17Fmw2aNTIbDxzAAq2IiIiIvJPpUvDr79CyZImvC5cyBNnzlC+dWuuxsXx448/mutOnwYfH/vW+hcFWxERERG5vY4dYetWqFULFz8/Xn31VTp37sxzzz1nWhYWLYI2bexdJaBgKyIiIiL/pndv8PQ082v37CEwMJD27dvjtnMnPPwwFCgA3brZu0pAwVZERERE/k3BgrByJZw9C5UqQblyUKqU2VyWkGCey5XL3lUC4GbvAkRERETEwd1/Pxw+DAsWwIYNNzaNtWxpxoM5CAVbEREREflv7u7Qrp35cFBqRRARERERp6BgKyIiIiJOQcFWRERERJyCgq2IiIiIOAUFWxERERFxCgq2IiIiIuIUFGxFRERExCk4XLCdP38+wcHB1K1bl/r167Nv3767er2IiIiIOCeHOqBh69atdO3alZCQEEqXLs20adNo3rw5+/fvx9fX946vFxERERHn5VArtiNHjqRly5aULl0agM6dO5OUlMSUKVPuyvUiIiIi4rwcKtiuXr2a6tWrX//axcWFatWqsWrVqrtyvYiIiIg4L4cJthcuXCAqKooCBQrc9HhgYCBhYWF3fP018fHxREVF3fQhIiIiIlmfwwTb2NhYADw9PW963NPT8/pzd3L9NSNGjMDf3//6R1BQ0J2WLiIiIiIOwGGCrZeXF2BWVP8uPj7++nN3cv01Q4YMITIy8vpHeHj4nZYuIiIiIg7AYaYi5M2bF39/f86cOXPT46dPn6ZEiRJ3fP01np6eN63yWpYFoJYEEREREQd1Laddy2234zDBFqBRo0aEhIRc/9qyLEJDQ3njjTfuyvW3Eh0dDaCWBBEREREHFx0djb+//22fd6hgO3jwYJo2bcrhw4cpVaoUM2fOxNXVla5duwJQp04d6tevz/Dhw1N1fWoUKlSI8PBwfH19sdlsREVFERQURHh4OH5+fhlyn5Kx9B5mfXoPsz69h85B72PW5yzvoWVZREdHU6hQoX+9zqGCbY0aNZgyZQodOnQgZ86cuLi4sHz58uuHLcTGxt7UU/tf16eGi4sLRYoU+cfjfn5+WfoPgOg9dAZ6D7M+vYfOQe9j1ucM7+G/rdReY7P+q1khm4mKisLf35/IyMgs/wcgu9J7mPXpPcz69B46B72PWV92ew8dZiqCiIiIiMidULD9P56engwbNuwf83El69B7mPXpPcz69B46B72PWV92ew/ViiAiIiIiTkErtiIiIiLiFBRsRURERMQpKNiKiIiIiFNQsBURERERp5Atg+38+fMJDg6mbt261K9fn3379t3V6yXjpeU9mTNnDs2aNaNx48YEBwfTrl07jh07lnnFyi2l97+rcePGYbPZWLNmTcYWKP8pre/h0aNHadOmDQ0bNuTee++lZs2abN++PZOqldtJy/sYHx9Pv379qFy5MvXr1+eBBx5g/vz5mVit/L+EhAQGDx6Mm5tbqv7ftmHDBmrWrEn9+vWpWbMm69evz/giM5OVzWzZssXy9fW1/vjjD8uyLGvq1KlW4cKFraioqLtyvWS8tL4n7u7u1rJlyyzLsqzk5GSrS5cuVtmyZa24uLhMq1lult7/rk6ePGkVLVrUAqxff/01EyqV20nre3j27FmrePHi1tq1ay3LsqzExESrYcOG1nfffZdpNcs/pfV9fPPNN63ixYtbly9ftizLskJDQy0PDw9r586dmVaz3BAWFmbVrFnTevrppy3ACgsL+9frjx07Zvn5+Vnr1q2zLMuy1qxZY/n5+VnHjh3LhGozR7YLtq1bt7Y6dOhw/evk5GSrQIEC1tixY+/K9ZLx0vqetG3b9qavt23bZgHWxo0bM7ROub30/nf1xBNPWBMmTFCwdQBpfQ/79+9vdezY8abHDh06ZJ08eTJD65R/l9b38ZFHHrHatWt302P58uWzRo0alaF1yq3t2bPHOnTokPXrr7+mKtj269fPqlmz5k2PBQcHW6+++moGVpm5sl0rwurVq6levfr1r11cXKhWrRqrVq26K9dLxkvrezJ37tybvs6RIwdgfqUm9pGe/64WLlyIu7s7zZs3z4wS5T+k9T388ccfqVev3k2PlSpVikKFCmVonfLv0vo+tmnThvXr13PixAkAli9fzrlz5yhQoECm1Cs3q1ixIqVKlUr19f//fgMEBwc7VabJVsH2woULREVF/eM/wMDAQMLCwu74esl4d+M92bRpE4UKFaJ27doZUaL8h/S8hzExMbzxxhuMHj06M0qU/5DW9zAmJoawsDCSk5Pp1KkTtWvXpnnz5ixdujSzSpZbSM9/i926dWPo0KFUqlSJ8uXL06JFC9q2bcuTTz6ZGSXLHTp69KjTZxo3exeQmWJjYwH+caycp6fn9efu5HrJeHf6nsTHx/Pxxx8zbtw43N3dM6RG+XfpeQ+HDh3KCy+8QMGCBbXxzwGk9T28fPkyYN7HX3/9lcqVK7N69err4bZp06YZXrP8U3r+W5w4cSIjR44kJCSEkiVLsmvXLlatWoWLS7ZaJ8uyYmNjnT7TZKs/iV5eXsA/fwUdHx9//bk7uV4y3p2+Jz179qR9+/a0bt06Q+qT/5bW9zA0NJQtW7bwwgsvZEp98t/S+h66uroC0KpVKypXrgxA48aNadSoEZ999lkGVyu3k9b30bIsBg4cSM+ePSlZsiQAlStXZsmSJXzwwQcZX7DcMS8vL6fPNNkq2ObNmxd/f3/OnDlz0+OnT5+mRIkSd3y9ZLw7eU8GDx6Ml5cX7733XkaWKP8hre/h4sWLuXr1Ko0aNaJBgwZ06NABgFdeeYUGDRpw+PDhTKlbbkjre5gvXz48PT0pXLjwTY8XK1bMqX4FmtWk9X08d+4cly5donjx4jc9fs899/DDDz9kZKlyl5QoUcLpM022CrYAjRo1IiQk5PrXlmURGhpKkyZN7sr1kvHS856MHDmS8PBwxo0bB0BISMhN30MyV1rew6FDhxIaGsqaNWtYs2YNs2fPBmDMmDGsWbMmTRsn5O5Jy3vo6upK7dq1iYiIuOnxM2fOULRo0QyvVW4vLe9jQEAAnp6e/3gfIyIinGrFz5k1btz4H//v2759u3NlGjtOZLCLLVu2WH5+ftahQ4csy7Ks6dOn3zSzr3bt2tbrr7+e6usl86X1PRw/frx17733Wps2bbK2bdtmbdu2zRo2bJg1efJke5QvVtrfw78LCwvTuC8HkNb3cPny5Vbu3Lmt48ePW5ZlWfv27bM8PT2thQsXZn7xcl1a38cePXpYZcuWtS5evGhZlmWFhIRY7u7u1pgxYzK/eLnuduO+OnbsaHXu3Pn619fm2G7YsMGyLMtat26d082xzVabxwBq1KjBlClT6NChAzlz5sTFxYXly5fj6+sLmMbqv/ef/Nf1kvnS8h5GR0fTp08fUlJSqFWr1k3fZ/LkyZleuxhp/e/wmldeeYXNmzdf/+dy5cpdX8GVzJXW97BZs2aMHTuWxx57DB8fH5KSkpg6dSqPPPKIvW5BSPv7OHr0aN5++20aN26Ml5cX0dHRjBw5kpdeeslet5CtJSQk0KxZs+sbNDt06EBQUND1MZdxcXE3bewrVqwYixYton///nh4eBAfH8+iRYsoVqyYPcrPEDbLsix7FyEiIiIicqeyXY+tiIiIiDgnBVsRERERcQoKtiIiIiLiFBRsRURERMQpKNiKiIiIiFNQsBURERERp6BgKyIiIiJOQcFWRERERJyCgq2IiIiIOAUFWxERJ/H5559TrFgx3NzcGDBggL3LERHJdDpSV0TECezatYvq1avz008/cf/99+Pv74+Xl5e9yxIRyVRu9i5ARETu3KJFi6hRowYtWrSwdykiInajYCsiksWVKlWKI0eOAGCz2ejSpQvTpk2zc1UiIplPrQgiIlnc2bNnqVWrFr169aJz5874+Pjg4+Nj77JERDKdNo+JiGRxPj4+HDt2jDp16hAYGEiXLl3InTs3bdu2tXdpIiKZSsFWRCSL2717NwD33XcfAC+//LJaEUQkW1KwFRHJ4nbu3EmpUqXw9vYGoEGDBvj6+tq5KhGRzKdgKyKSxe3cuZPKlSvbuwwREbtTsBURyeJ27txJlSpV7F2GiIjdKdiKiGRhKSkp7NmzRyu2IiJojq2ISJbm4uJCTEyMvcsQEXEImmMrIuJkmjRpwq5du4iJiSFPnjzMnTuXWrVq2bssEZEMp2ArIiIiIk5BPbYiIiIi4hQUbEVERETEKSjYioiIiIhTULAVEREREaegYCsiIiIiTkHBVkREREScgoKtiIiIiDgFBVsRERERcQoKtiIiIiLiFBRsRURERMQpKNiKiIiIiFP4H+oDvh3YLWOpAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Decision Making"],"metadata":{"id":"LON5vZwCPvKO"}},{"cell_type":"code","source":["# Without any preference\n","dm = HighTradeoffPoints()\n","I = dm(res.F)\n","\n","plot = Scatter()\n","plot.add(res.F, alpha=0.2)\n","plot.add(res.F[I], color=\"red\", s=100)\n","plot.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":563},"id":"kI19bq0BPGCl","executionInfo":{"status":"ok","timestamp":1687201957783,"user_tz":-480,"elapsed":1681,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"6ace8905-1c4c-461c-a475-d19161ffc44f"},"execution_count":90,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<pymoo.visualization.scatter.Scatter at 0x7f3e5d1cde10>"]},"metadata":{},"execution_count":90},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# With preference: say both objectives are equally important\n","weights = np.array([0.9, 0.1])\n","decomp = ASF()\n","I = decomp(res.F, weights).argmin()\n","\n","plot = Scatter()\n","plot.add(res.F, color=\"blue\", alpha=0.2, s=10)\n","plot.add(res.F[I], color=\"red\", s=30)\n","plot.do()\n","plot.apply(lambda ax: ax.arrow(0, 0, 1.2*weights[0], 1.2*weights[1], color='black',\n","                               head_width=0.01, head_length=0.01, alpha=0.4))\n","plot.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":563},"id":"CQWksh8BP9vw","executionInfo":{"status":"ok","timestamp":1687202005416,"user_tz":-480,"elapsed":1008,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"f04e44ab-ee3e-4823-da9e-0bd75a32b45b"},"execution_count":92,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<pymoo.visualization.scatter.Scatter at 0x7f3e5778a2c0>"]},"metadata":{},"execution_count":92},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# An Example Tri-objective Problem"],"metadata":{"id":"rGwabHOMi91R"}},{"cell_type":"code","source":["from pymoo.algorithms.moo.nsga3 import NSGA3\n","\n","problem = get_problem(\"dtlz2\")\n","ref_dirs = get_reference_directions(\"das-dennis\", 3, n_partitions=12)\n","algorithm = NSGA3(pop_size=92, ref_dirs=ref_dirs)\n","\n","res = minimize(problem,\n","               algorithm,\n","               seed=1,\n","               termination=('n_gen', 600))\n","\n","Scatter().add(res.F).show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"id":"_lDFrMKcjFJ0","executionInfo":{"status":"ok","timestamp":1687193302564,"user_tz":-480,"elapsed":11371,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"99e6b51b-3e49-4818-bb7d-cb54daaa81d6"},"execution_count":80,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<pymoo.visualization.scatter.Scatter at 0x7f3e6ab37370>"]},"metadata":{},"execution_count":80},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Define your own problem"],"metadata":{"id":"t8MJ72DDRufZ"}},{"cell_type":"code","source":["from pymoo.core.problem import Problem\n","\n","class MyProblem(Problem):\n","\n","    def __init__(self):\n","        super().__init__(n_var=1, n_obj=2, vtype=float)\n","        self.xl = np.zeros(self.n_var)\n","        self.xu = np.ones(self.n_var)\n","\n","    def _evaluate(self, x, out, *args, **kwargs):\n","        f1 = x\n","        f2 = 1-x\n","\n","        out[\"F\"] = np.column_stack([f1, f2])"],"metadata":{"id":"9sQTJvkNRuAz","executionInfo":{"status":"ok","timestamp":1687202111932,"user_tz":-480,"elapsed":439,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}}},"execution_count":93,"outputs":[]},{"cell_type":"code","source":["problem = MyProblem()\n","\n","res = minimize(problem,\n","               algorithm,\n","               ('n_gen', 10),\n","               seed=1,\n","               verbose=True)\n","\n","plot = Scatter()\n","plot.add(problem.pareto_front(), plot_type=\"line\", color=\"black\", alpha=0.7)\n","plot.add(res.F, facecolor=\"none\", edgecolor=\"red\")\n","plot.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":795},"id":"oVA9Vjz8U61N","executionInfo":{"status":"ok","timestamp":1687202117378,"user_tz":-480,"elapsed":1035,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"6428dde3-9edd-4a32-ba10-45df7ba77fc4"},"execution_count":94,"outputs":[{"output_type":"stream","name":"stdout","text":["==========================================================\n","n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n","==========================================================\n","     1 |      100 |    100 |             - |             -\n","     2 |      200 |    100 |  0.0051327988 |         ideal\n","     3 |      300 |    100 |  0.0032051211 |             f\n","     4 |      400 |    100 |  0.0017590520 |             f\n","     5 |      500 |    100 |  0.0044816205 |         ideal\n","     6 |      600 |    100 |  0.0022051173 |             f\n","     7 |      700 |    100 |  0.0030179362 |             f\n","     8 |      800 |    100 |  0.0018623728 |             f\n","     9 |      900 |    100 |  0.0033532239 |             f\n","    10 |     1000 |    100 |  0.0019989564 |             f\n"]},{"output_type":"execute_result","data":{"text/plain":["<pymoo.visualization.scatter.Scatter at 0x7f3e577d57e0>"]},"metadata":{},"execution_count":94},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Create your own EMO algorithm"],"metadata":{"id":"KnebmtqRTcfn"}},{"cell_type":"code","source":["from pymoo.algorithms.base.genetic import GeneticAlgorithm\n","from pymoo.algorithms.moo.nsga2 import *\n","\n","\n","class MyEMOAlgorithm(GeneticAlgorithm):\n","\n","    def __init__(self,\n","                 pop_size=100,\n","                 sampling=\"How do you initialize a population\",\n","                 selection=\"How do you select parents\",\n","                 crossover=\"How do you exchange information between parents to make new offspring\",\n","                 mutation=\"How do you alter a solution\",\n","                 survival=\"How do you select a subset solutions from population\",\n","                 output=MultiObjectiveOutput(),\n","                 **kwargs):\n","\n","        super().__init__(\n","            pop_size=pop_size,\n","            sampling=sampling,\n","            selection=selection,\n","            crossover=crossover,\n","            mutation=mutation,\n","            survival=survival,\n","            output=output,\n","            advance_after_initial_infill=True,\n","            **kwargs)\n","\n","        self.termination = DefaultMultiObjectiveTermination()\n","        self.tournament_type = 'comp_by_dom_and_crowding'\n","\n","    def _set_optimum(self, **kwargs):\n","        if not has_feasible(self.pop):\n","            self.opt = self.pop[[np.argmin(self.pop.get(\"CV\"))]]\n","        else:\n","            self.opt = self.pop[self.pop.get(\"rank\") == 0]"],"metadata":{"id":"e1YeM7oATY2g","executionInfo":{"status":"ok","timestamp":1687189389678,"user_tz":-480,"elapsed":2,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}}},"execution_count":57,"outputs":[]},{"cell_type":"code","source":["algorithm = MyEMOAlgorithm(pop_size=100)\n","\n","problem = get_problem(\"tnk\")\n","\n","res = minimize(problem,\n","               algorithm,\n","               ('n_gen', 10),\n","               seed=1,\n","               verbose=True)\n","\n","plot = Scatter()\n","plot.add(problem.pareto_front(), plot_type=\"line\", color=\"black\", alpha=0.7)\n","plot.add(res.F, facecolor=\"none\", edgecolor=\"red\")\n","plot.show()"],"metadata":{"id":"G6tUmcIWTY9D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"noW8Th15TZBP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ZJXWWAyHTZEx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"u44CYaAZSlzI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sz5JtLtGCVUc"},"source":["# Introduction to EvoXBench\n","\n","In this notebook, we will demonstrate\n","- how to install EvoXBench\n","- the basics of EvoXBench\n","\n","**[EvoXBench](https://arxiv.org/abs/2208.04321)** is an efficient platform\n","for facilitating neural architecture search (NAS)\n","without the requirement of *GPUs* or\n","sophisticated deep learning packages, such as *PyTorch, TensorFlow*, etc.\n","\n","![](https://raw.githubusercontent.com/EMI-Group/evoxbench/main/assets/evoxbench_overview.png)"]},{"cell_type":"markdown","metadata":{"id":"eFN1qRkMCVUe"},"source":["## 1. Preparation\n","Let's perform the following steps to have EvoXBench properly installed.\n","\n","First, download the following two files:\n","- ``database_xxx.zip`` from [Google Drive](https://drive.google.com/file/d/11bQ1paHEWHDnnTPtxs2OyVY_Re-38DiO/view?usp=sharing) or [Baidu NetDisk](https://pan.baidu.com/s/1PwWloA543-81O-GFkA7GKg)\n","- ``data_xxx.zip`` from [Google Drive](https://drive.google.com/file/d/1fUZtpTjfEQao2unLKaspL8fOq4xdSXt2/view?usp=sharing) or [Baidu NetDisk](https://pan.baidu.com/s/1yopkISKyjbWIHXFV_Op3pg)\n","\n","Second, unzip these two files and find their paths\n","- my ``database`` and ``data`` are unzipped to:\n","```python\n","    # /Users/luzhicha/Dropbox/2023/github/evoxbench/\n","    # └─ database/\n","    # |  |  __init__.py\n","    # |  |  db.sqlite3\n","    # |  |  ...\n","    # |\n","    # └─ data/\n","    #    └─ darts/\n","    #    └─ mnv3/\n","    #    └─ ...\n","```\n","\n"]},{"cell_type":"code","execution_count":95,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KyUKhE8qCVUe","executionInfo":{"status":"ok","timestamp":1687202304914,"user_tz":-480,"elapsed":5027,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"61e22b3f-4713-4768-b39c-10c147220d2d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Installing EvoXBench...\n"]}],"source":["print('Installing EvoXBench...')\n","! pip install evoxbench 1>/dev/null\n","# ! pip install git+https://github.com/EMI-Group/evoxbench"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# !unzip drive/MyDrive/database20220713.zip\n","# !unzip drive/MyDrive/data20221028.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"koqBDgIUaOi8","executionInfo":{"status":"ok","timestamp":1687202314996,"user_tz":-480,"elapsed":2587,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"e4adf238-0355-46cd-fc6c-3ae42724b540"},"execution_count":96,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","execution_count":97,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jZCV7dN6CVUf","executionInfo":{"status":"ok","timestamp":1687202335108,"user_tz":-480,"elapsed":424,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"dd700821-451d-47ae-d7d4-4c743505ff74"},"outputs":[{"output_type":"stream","name":"stdout","text":["Configurating EvoXBench...\n","Configuration Succeed!\n"]}],"source":["print('Configurating EvoXBench...')\n","from evoxbench.database.init import config\n","# make sure you update these two paths accordingly, and the first path should be for database file\n","config(\"database\", \"data\")"]},{"cell_type":"markdown","metadata":{"id":"TNUIIfReCVUf"},"source":["Good! Now we have successfully installed and configured **EvoXBench**. Let's now get started with some quick examples."]},{"cell_type":"markdown","metadata":{"id":"8Ci7kqLbCVUg"},"source":["# 2.1 How to create a NAS benchmark (search space)\n","\n","**EvoXBench** currently supports the following seven search spaces\n","\n","| $\\Omega$ | $D$ | $|\\Omega|$ | Objectives | Dataset |\n","|:-:|:-:|:-:|:-:|:-:|\n","| [NB101](https://github.com/google-research/nasbench) | 26 |423K | $f^{e}$, ${f}^{c}$ | CIFAR-10 |\n","| [NB201](https://github.com/D-X-Y/NAS-Bench-201) | 6 | 15.6K | $f^{e}$, ${f}^{c}$, ${f}^{\\mathcal{H}}$ | CIFAR-10 |\n","| [NATS](https://github.com/D-X-Y/NATS-Bench) | 5 | 32.8K | $f^{e}$, ${f}^{c}$, ${f}^{\\mathcal{H}}$ | CIFAR-10 |\n","| [DARTS](https://github.com/automl/nasbench301) | 32 | $\\sim10^{21}$ | $f^{e}$, ${f}^{c}$ | CIFAR-10 |\n","| [ResNet-50](https://github.com/mit-han-lab/once-for-all) | 25 | $\\sim10^{14}$ | $f^{e}$, ${f}^{c}$ | ImageNet-1K |\n","| [Transformer](https://github.com/microsoft/Cream/tree/main/AutoFormer) | 34 | $\\sim10^{14}$ | $f^{e}$, ${f}^{c}$ | ImageNet-1K |\n","| [MNV3](https://github.com/mit-han-lab/once-for-all) | 21 | $\\sim10^{20}$ | $f^{e}$, ${f}^{c}$, ${f}^{\\mathcal{H}}$ | ImageNet-1K |\n"]},{"cell_type":"code","execution_count":98,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xnveMKDMCVUg","executionInfo":{"status":"ok","timestamp":1687202425052,"user_tz":-480,"elapsed":440,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"b1138f16-7982-48ba-f2b3-62b0b2effa6a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Benchmaking on NB101 search space with objectives: err&params\n"]}],"source":["# NAS-Bench-101 search space\n","from evoxbench.benchmarks import NASBench101Benchmark\n","objs = 'err&params'  # ['err&params', 'err&flops', 'err&params&flops']\n","benchmark = NASBench101Benchmark(objs=objs, normalized_objectives=False)\n","print(\"Benchmaking on NB101 search space with objectives: {}\".format(objs))\n","\n","# # NAS-Bench-201 search space\n","# from evoxbench.benchmarks import NASBench201Benchmark\n","# # hardware = 'edgegpu'  # ['edgegpu', 'raspi4', 'edgetpu', 'pixel3', 'eyeriss', 'fpga']\n","# # ['err&params', 'err&flops', 'err&latency', 'err&params&flops', 'err&params&latency', ...]\n","# objs = 'err&params&flops&edgegpu_latency&edgegpu_energy'\n","# benchmark = NASBench201Benchmark(objs=objs, normalized_objectives=False)\n","# print(\"Benchmaking on NB201 search space with objectives: {}\".format(objs))\n","\n","# # NATS size search space\n","# from evoxbench.benchmarks import NATSBenchmark\n","# objs = 'err&params&flops&latency'\n","# # ['err&params', 'err&flops', 'err&latency', 'err&params&flops', 'err&params&latency', ...]\n","# benchmark = NATSBenchmark(objs=objs, normalized_objectives=False)\n","# print(\"Benchmaking on NATS search space with objectives: {}\".format(objs))\n","\n","# # DARTS search space\n","# from evoxbench.benchmarks import DARTSBenchmark\n","# objs = 'err&params'  # ['err&params', 'err&flops', 'err&params&flops']\n","# benchmark = DARTSBenchmark(objs=objs, normalized_objectives=False)\n","# print(\"Benchmaking on DARTS search space with objectives: {}\".format(objs))"]},{"cell_type":"markdown","metadata":{"id":"3WFxCJMuCVUh"},"source":["# 2.2 How to evaluate an architecture"]},{"cell_type":"code","execution_count":100,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vWJ_PGFDCVUh","executionInfo":{"status":"ok","timestamp":1687202437365,"user_tz":-480,"elapsed":459,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"bedb02fa-2e36-458a-eb30-da70362ea602"},"outputs":[{"output_type":"stream","name":"stdout","text":["Randomly create 5 architectures:\n","[{'matrix': array([[0, 0, 1, 1, 0, 1, 0],\n","       [0, 0, 1, 1, 1, 1, 0],\n","       [0, 0, 0, 1, 1, 0, 0],\n","       [0, 0, 0, 0, 1, 0, 0],\n","       [0, 0, 0, 0, 0, 0, 1],\n","       [0, 0, 0, 0, 0, 0, 0],\n","       [0, 0, 0, 0, 0, 0, 0]]), 'ops': ['input', 'conv1x1-bn-relu', 'conv3x3-bn-relu', 'maxpool3x3', 'maxpool3x3', 'conv1x1-bn-relu', 'output']}, {'matrix': array([[0, 1, 1, 1, 1, 0, 0],\n","       [0, 0, 0, 1, 1, 0, 1],\n","       [0, 0, 0, 1, 1, 0, 0],\n","       [0, 0, 0, 0, 1, 1, 1],\n","       [0, 0, 0, 0, 0, 0, 0],\n","       [0, 0, 0, 0, 0, 0, 0],\n","       [0, 0, 0, 0, 0, 0, 0]]), 'ops': ['input', 'conv1x1-bn-relu', 'conv1x1-bn-relu', 'conv3x3-bn-relu', 'conv1x1-bn-relu', 'conv1x1-bn-relu', 'output']}, {'matrix': array([[0, 1, 1, 0, 0, 1, 1],\n","       [0, 0, 1, 0, 0, 0, 1],\n","       [0, 0, 0, 0, 0, 1, 0],\n","       [0, 0, 0, 0, 1, 1, 0],\n","       [0, 0, 0, 0, 0, 1, 0],\n","       [0, 0, 0, 0, 0, 0, 1],\n","       [0, 0, 0, 0, 0, 0, 0]]), 'ops': ['input', 'conv3x3-bn-relu', 'conv3x3-bn-relu', 'maxpool3x3', 'maxpool3x3', 'conv3x3-bn-relu', 'output']}, {'matrix': array([[0, 1, 0, 0, 0, 1, 0],\n","       [0, 0, 1, 0, 0, 0, 0],\n","       [0, 0, 0, 1, 0, 0, 0],\n","       [0, 0, 0, 0, 0, 0, 1],\n","       [0, 0, 0, 0, 0, 0, 1],\n","       [0, 0, 0, 0, 0, 0, 0],\n","       [0, 0, 0, 0, 0, 0, 0]]), 'ops': ['input', 'maxpool3x3', 'conv3x3-bn-relu', 'maxpool3x3', 'conv1x1-bn-relu', 'conv3x3-bn-relu', 'output']}, {'matrix': array([[0, 1, 0, 0, 1, 0, 0],\n","       [0, 0, 0, 0, 1, 1, 1],\n","       [0, 0, 0, 0, 0, 1, 0],\n","       [0, 0, 0, 0, 0, 1, 1],\n","       [0, 0, 0, 0, 0, 1, 0],\n","       [0, 0, 0, 0, 0, 0, 1],\n","       [0, 0, 0, 0, 0, 0, 0]]), 'ops': ['input', 'conv1x1-bn-relu', 'maxpool3x3', 'conv3x3-bn-relu', 'maxpool3x3', 'conv1x1-bn-relu', 'output']}]\n"]}],"source":["# let's randomly create N architectures\n","N = 5\n","archs = benchmark.search_space.sample(N)\n","print('Randomly create {} architectures:'.format(N))\n","print(archs)"]},{"cell_type":"code","execution_count":101,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aWj5PQjxCVUh","executionInfo":{"status":"ok","timestamp":1687202454266,"user_tz":-480,"elapsed":433,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"10c44668-f1c8-4a5a-d6b6-bed9c8deb8c2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Encode architectures to decision variables X: \n","[[0 1 1 0 1 0 1 1 1 1 0 1 1 0 0 1 0 0 0 1 0 1 0 2 2 1]\n"," [1 1 1 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 0 0 1 1 0 1 1]\n"," [1 1 0 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 1 0 1 0 0 2 2 0]\n"," [1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 2 0 2 1 0]\n"," [1 0 0 1 0 0 0 0 1 1 1 0 0 1 0 0 1 1 1 0 1 1 2 0 2 1]]\n"]}],"source":["# encode architecture (phenotype) to decision variables (genotypes)\n","X = benchmark.search_space.encode(archs)\n","print('Encode architectures to decision variables X: ')\n","print(X)"]},{"cell_type":"code","execution_count":102,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WDuli4NtCVUi","executionInfo":{"status":"ok","timestamp":1687202477967,"user_tz":-480,"elapsed":464,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"43414795-0872-494b-8517-0100732b8b78"},"outputs":[{"output_type":"stream","name":"stdout","text":["Evaluating architectures for objectives: err&params\n","[[1.00727836e-01 1.10514020e+07]\n"," [7.09802310e-02 4.16602600e+06]\n"," [6.69070482e-02 9.16852200e+06]\n"," [9.73557631e-02 1.01776740e+07]\n"," [1.10076129e-01 1.40404200e+06]]\n"]}],"source":["# Evaluate the objective values\n","# if true_eval is True, return mean TEST accuracy over multiple runs,\n","# should only be used for final comparison.\n","true_eval = True\n","F = benchmark.evaluate(X, true_eval=true_eval)\n","print(\"Evaluating architectures for objectives: {}\".format(objs))\n","print(F)"]},{"cell_type":"markdown","source":["# Let's define the multi-objective NAS problem"],"metadata":{"id":"hUiazXlxgwOm"}},{"cell_type":"code","source":["# some utility functions\n","\n","from pymoo.operators.crossover.sbx import SBX\n","from pymoo.operators.mutation.pm import PM\n","from pymoo.operators.sampling.rnd import IntegerRandomSampling\n","from pymoo.operators.repair.rounding import RoundingRepair\n","\n","\n","def get_genetic_operator(crx_prob=1.0,  # crossover probability\n","                         crx_eta=30.0,  # SBX crossover eta\n","                         mut_prob=0.9,  # mutation probability\n","                         mut_eta=20.0,  # polynomial mutation hyperparameter eta\n","                         ):\n","    sampling = IntegerRandomSampling()\n","    crossover = SBX(prob=crx_prob, eta=crx_eta, repair=RoundingRepair(), vtype=int)\n","    mutation = PM(prob=mut_prob, eta=mut_eta, repair=RoundingRepair(), vtype=int)\n","    return sampling, crossover, mutation\n","\n","def get_benchmark_settings(n_obj):\n","    n_gen = 100\n","\n","    if n_obj == 2:\n","        ref_dirs = get_reference_directions(\"das-dennis\", n_obj, n_partitions=99)\n","    elif n_obj == 3:\n","        ref_dirs = get_reference_directions(\"das-dennis\", n_obj, n_partitions=13)\n","    elif n_obj == 4:\n","        ref_dirs = get_reference_directions(\"das-dennis\", n_obj, n_partitions=7)\n","    elif n_obj == 5:\n","        ref_dirs = get_reference_directions(\"das-dennis\", n_obj, n_partitions=5)\n","    elif n_obj == 6:\n","        ref_dirs = get_reference_directions(\n","            \"multi-layer\",\n","            get_reference_directions(\"das-dennis\", n_obj, n_partitions=4, scaling=1.0),\n","            get_reference_directions(\"das-dennis\", n_obj, n_partitions=1, scaling=0.5))\n","    elif n_obj == 8:\n","        ref_dirs = get_reference_directions(\n","            \"multi-layer\",\n","            get_reference_directions(\"das-dennis\", n_obj, n_partitions=3, scaling=1.0),\n","            get_reference_directions(\"das-dennis\", n_obj, n_partitions=2, scaling=0.5))\n","    else:\n","        raise NotImplementedError\n","\n","    pop_size = ref_dirs.shape[0]\n","\n","    return pop_size, n_gen, ref_dirs\n",""],"metadata":{"id":"__EJ-EGIhO8U","executionInfo":{"status":"ok","timestamp":1687202506717,"user_tz":-480,"elapsed":433,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}}},"execution_count":103,"outputs":[]},{"cell_type":"code","execution_count":104,"metadata":{"id":"rFcL-H-bCVUi","executionInfo":{"status":"ok","timestamp":1687202536630,"user_tz":-480,"elapsed":428,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}}},"outputs":[],"source":["# define the NAS problem using Pymoo\n","class MONASProblem(Problem):\n","    def __init__(self,\n","                 benchmark,\n","                 **kwargs):\n","        super().__init__(n_var=benchmark.search_space.n_var,\n","                         n_obj=benchmark.evaluator.n_objs,\n","                         n_constr=0, xl=benchmark.search_space.lb,\n","                         xu=benchmark.search_space.ub,\n","                         type_var=np.int64, **kwargs)\n","\n","        self.benchmark = benchmark\n","\n","    def _evaluate(self, x, out, *args, **kwargs):\n","\n","        F = self.benchmark.evaluate(x, true_eval=True)\n","\n","        out[\"F\"] = F"]},{"cell_type":"code","execution_count":105,"metadata":{"id":"8tikuh79CVUi","executionInfo":{"status":"ok","timestamp":1687202543787,"user_tz":-480,"elapsed":439,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}}},"outputs":[],"source":["# define the EMO algorithm\n","def nsga2(pop_size,\n","          crx_prob=1.0,  # crossover probability\n","          crx_eta=30.0,  # SBX crossover eta\n","          mut_prob=0.9,  # mutation probability\n","          mut_eta=20.0,  # polynomial mutation hyperparameter eta\n","          ):\n","\n","    sampling, crossover, mutation = get_genetic_operator(crx_prob, crx_eta, mut_prob, mut_eta)\n","\n","    return NSGA2(pop_size=pop_size, sampling=sampling, crossover=crossover,\n","        mutation=mutation, eliminate_duplicates=True)"]},{"cell_type":"markdown","source":["# Now we are ready to kick off the search"],"metadata":{"id":"vdMBSgAShuBS"}},{"cell_type":"code","source":["problem = MONASProblem(benchmark)\n","pop_size, n_gen, ref_dirs = get_benchmark_settings(problem.n_obj)\n","\n","algorithm = nsga2(pop_size)\n","\n","res = minimize(problem, algorithm, ('n_gen', n_gen), verbose=True)\n","\n","F = benchmark.evaluate(res.X, true_eval=True)  # re-evaluate the final population for test accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nJQY1HO3e8ZI","executionInfo":{"status":"ok","timestamp":1687202560823,"user_tz":-480,"elapsed":11260,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"23af0402-259b-47cc-b015-2eb9b200cdf6"},"execution_count":106,"outputs":[{"output_type":"stream","name":"stdout","text":["==========================================================\n","n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n","==========================================================\n","     1 |      100 |     10 |             - |             -\n","     2 |      200 |     19 |  0.0076539493 |             f\n","     3 |      300 |     30 |  0.2524221729 |         nadir\n","     4 |      400 |     36 |  0.0052477525 |             f\n","     5 |      500 |     49 |  0.0015957242 |             f\n","     6 |      600 |     55 |  0.0034429507 |             f\n","     7 |      700 |     79 |  0.0001861469 |             f\n","     8 |      800 |     96 |  0.0005090910 |             f\n","     9 |      900 |    100 |  0.0009492900 |             f\n","    10 |     1000 |    100 |  0.0009492900 |             f\n","    11 |     1100 |    100 |  0.0009492900 |             f\n","    12 |     1200 |    100 |  0.0009492900 |             f\n","    13 |     1300 |    100 |  0.0009900877 |             f\n","    14 |     1400 |    100 |  0.0014215379 |             f\n","    15 |     1500 |    100 |  0.0019102653 |             f\n","    16 |     1600 |    100 |  0.0015685940 |             f\n","    17 |     1700 |    100 |  0.0013450364 |             f\n","    18 |     1800 |    100 |  0.0013450364 |             f\n","    19 |     1900 |    100 |  0.0016867077 |             f\n","    20 |     2000 |    100 |  0.0012744819 |             f\n","    21 |     2100 |    100 |  0.0014125975 |             f\n","    22 |     2200 |    100 |  0.0014125975 |             f\n","    23 |     2300 |    100 |  0.0014125975 |             f\n","    24 |     2400 |    100 |  0.0015272783 |             f\n","    25 |     2500 |    100 |  0.0019505450 |             f\n","    26 |     2600 |    100 |  0.0019505450 |             f\n","    27 |     2700 |    100 |  0.0020478630 |             f\n","    28 |     2800 |    100 |  0.0019824230 |             f\n","    29 |     2900 |    100 |  0.0020478630 |             f\n","    30 |     3000 |    100 |  0.0020886607 |             f\n","    31 |     3100 |    100 |  0.0020886607 |             f\n","    32 |     3200 |    100 |  0.0018851050 |             f\n","    33 |     3300 |    100 |  0.0019824230 |             f\n","    34 |     3400 |    100 |  0.0145725977 |         ideal\n","    35 |     3500 |    100 |  0.000000E+00 |             f\n","    36 |     3600 |    100 |  0.000000E+00 |             f\n","    37 |     3700 |    100 |  0.000000E+00 |             f\n","    38 |     3800 |    100 |  0.000000E+00 |             f\n","    39 |     3900 |    100 |  0.000000E+00 |             f\n","    40 |     4000 |    100 |  0.000000E+00 |             f\n","    41 |     4100 |    100 |  0.000000E+00 |             f\n","    42 |     4200 |    100 |  0.0002371500 |             f\n","    43 |     4300 |    100 |  0.0002371500 |             f\n","    44 |     4400 |    100 |  0.0002371500 |             f\n","    45 |     4500 |    100 |  0.0002371500 |             f\n","    46 |     4600 |    100 |  0.0002371500 |             f\n","    47 |     4700 |    100 |  0.0002371500 |             f\n","    48 |     4800 |    100 |  0.0002371500 |             f\n","    49 |     4900 |    100 |  0.0002371500 |             f\n","    50 |     5000 |    100 |  0.0002371500 |             f\n","    51 |     5100 |    100 |  0.0002371500 |             f\n","    52 |     5200 |    100 |  0.0007114501 |             f\n","    53 |     5300 |    100 |  0.0007114501 |             f\n","    54 |     5400 |    100 |  0.0007114501 |             f\n","    55 |     5500 |    100 |  0.0009486002 |             f\n","    56 |     5600 |    100 |  0.0007114501 |             f\n","    57 |     5700 |    100 |  0.0007114501 |             f\n","    58 |     5800 |    100 |  0.0007114501 |             f\n","    59 |     5900 |    100 |  0.0004743001 |             f\n","    60 |     6000 |    100 |  0.0002371500 |             f\n","    61 |     6100 |    100 |  0.0002371500 |             f\n","    62 |     6200 |    100 |  0.0002371500 |             f\n","    63 |     6300 |    100 |  0.0002371500 |             f\n","    64 |     6400 |    100 |  0.0002371500 |             f\n","    65 |     6500 |    100 |  0.0002371500 |             f\n","    66 |     6600 |    100 |  0.0002371500 |             f\n","    67 |     6700 |    100 |  0.0002371500 |             f\n","    68 |     6800 |    100 |  0.0002371500 |             f\n","    69 |     6900 |    100 |  0.0004743001 |             f\n","    70 |     7000 |    100 |  0.0002371500 |             f\n","    71 |     7100 |    100 |  0.0002371500 |             f\n","    72 |     7200 |    100 |  0.0002371500 |             f\n","    73 |     7300 |    100 |  0.0002371500 |             f\n","    74 |     7400 |    100 |  0.0002371500 |             f\n","    75 |     7500 |    100 |  0.0002371500 |             f\n","    76 |     7600 |    100 |  0.0002371500 |             f\n","    77 |     7700 |    100 |  0.0002371500 |             f\n","    78 |     7800 |    100 |  0.0002371500 |             f\n","    79 |     7900 |    100 |  0.0002371500 |             f\n","    80 |     8000 |    100 |  0.0002371500 |             f\n","    81 |     8100 |    100 |  0.0002371500 |             f\n","    82 |     8200 |    100 |  0.0002371500 |             f\n","    83 |     8300 |    100 |  0.0002371500 |             f\n","    84 |     8400 |    100 |  0.0004743001 |             f\n","    85 |     8500 |    100 |  0.0002371500 |             f\n","    86 |     8600 |    100 |  0.0002371500 |             f\n","    87 |     8700 |    100 |  0.0004743001 |             f\n","    88 |     8800 |    100 |  0.0004743001 |             f\n","    89 |     8900 |    100 |  0.0004743001 |             f\n","    90 |     9000 |    100 |  0.0007114501 |             f\n","    91 |     9100 |    100 |  0.0004743001 |             f\n","    92 |     9200 |    100 |  0.0004743001 |             f\n","    93 |     9300 |    100 |  0.0004743001 |             f\n","    94 |     9400 |    100 |  0.0007114501 |             f\n","    95 |     9500 |    100 |  0.0007114501 |             f\n","    96 |     9600 |    100 |  0.0007114501 |             f\n","    97 |     9700 |    100 |  0.0007114501 |             f\n","    98 |     9800 |    100 |  0.0002371500 |             f\n","    99 |     9900 |    100 |  0.0002371500 |             f\n","   100 |    10000 |    100 |  0.0004743001 |             f\n"]}]},{"cell_type":"code","source":["# calculate performance metrics\n","hv = benchmark.calc_perf_indicator(res.X, 'hv')\n","\n","# visualize the solutions\n","plot = Scatter()\n","pf = benchmark.pareto_front\n","sort_idx = np.argsort(pf[:, 0])\n","\n","# plot.add(pf[sort_idx], plot_type=\"line\", color=\"black\", alpha=0.7)\n","plot.add(pf[sort_idx], facecolor=\"none\", edgecolor=\"black\", alpha=0.7)\n","\n","plot.add(F, facecolor=\"none\", edgecolor=\"red\")\n","plot.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":578},"id":"DA97AYjAh1DZ","executionInfo":{"status":"ok","timestamp":1687202571870,"user_tz":-480,"elapsed":491,"user":{"displayName":"Zhichao Lu","userId":"07419631455947028017"}},"outputId":"00e7badd-ca4c-4239-8af8-c4c73a68c235"},"execution_count":107,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<pymoo.visualization.scatter.Scatter at 0x7f3e5d563850>"]},"metadata":{},"execution_count":107},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"d3cfPIociBIy"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0}