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pymoo – NSGA-II: Non-dominated Sorting Genetic Algorithm
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- Summary of article content: Articles about pymoo – NSGA-II: Non-dominated Sorting Genetic Algorithm Each indivual is first compared by rank and then crowding distance. There is also a variant in the original C code where instead of using the rank, the … …
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Example¶
API¶
Notebooks
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- Summary of article content: Articles about Notebooks The code below implements the NSGA algorithm as explained in 6. Multi Objective Optimization & NSGA II.ipynb . In [1]:. …
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Google Code Archive – Long-term storage for Google Code Project Hosting.
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Google Code Archive – Long-term storage for Google Code Project Hosting.
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- Summary of article content: Articles about Google Code Archive – Long-term storage for Google Code Project Hosting. There are several steps of Non-dominated Sorting Algorithm. I also added some comments in the code. Make a python dictionary with fitness values … …
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NSGA-II: Non-dominated Sorting Genetic Algorithm
NSGA-II: Non-dominated Sorting Genetic Algorithm¶
The algorithm is implemented based on [4]. The algorithm follows the general outline of a genetic algorithm with a modified mating and survival selection. In NSGA-II, first, individuals are selected frontwise. By doing so, there will be the situation where a front needs to be split because not all individuals are allowed to survive. In this splitting front, solutions are selected based on crowding distance.
The crowding distance is the Manhatten Distance in the objective space. However, the extreme points are desired to be kept every generation and, therefore, get assigned a crowding distance of infinity.
Furthermore, to increase some selection pressure, NSGA-II uses a binary tournament mating selection. Each individual is first compared by rank and then crowding distance. There is also a variant in the original C code where instead of using the rank, the domination criterium between two solutions is used.
Example¶ [1]: from pymoo.algorithms.moo.nsga2 import NSGA2 from pymoo.factory import get_problem from pymoo.optimize import minimize from pymoo.visualization.scatter import Scatter problem = get_problem ( “zdt1” ) algorithm = NSGA2 ( pop_size = 100 ) res = minimize ( problem , algorithm , ( ‘n_gen’ , 200 ), seed = 1 , verbose = False ) plot = Scatter () plot . add ( problem . pareto_front (), plot_type = “line” , color = “black” , alpha = 0.7 ) plot . add ( res . F , facecolor = “none” , edgecolor = “red” ) plot . show () [1]:
Moreover, we can customize NSGA-II to solve a problem with binary decision variables, for example, ZDT5. [2]: from pymoo.algorithms.moo.nsga2 import NSGA2 from pymoo.factory import get_problem , get_sampling , get_crossover , get_mutation from pymoo.optimize import minimize from pymoo.visualization.scatter import Scatter problem = get_problem ( “zdt5” ) algorithm = NSGA2 ( pop_size = 100 , sampling = get_sampling ( “bin_random” ), crossover = get_crossover ( “bin_two_point” ), mutation = get_mutation ( “bin_bitflip” ), eliminate_duplicates = True ) res = minimize ( problem , algorithm , ( ‘n_gen’ , 500 ), seed = 1 , verbose = False ) plot = Scatter () plot . add ( res . F , facecolor = “none” , edgecolor = “red” ) plot . show () [2]:
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