from OptimizationAlgorithms import genetic as g from problem3 import problem as p import matplotlib.pyplot as plt pr = p.Problem() population_size = 1000 number_of_generations = 20 mutation_rate = 0.02 tornument_size = [2, 5, 10] plot = False costs = [] scores = [] for t_siz in tornument_size: sol = g.genetic(pr, population_size, number_of_generations, mutation_rate, t_siz, plot) costs.append(sol.get('cost')) scores.append(sol.get('score')) print('state = {} , score = {} , cost = {} '.format( sol.get('state'), sol.get('score'), sol.get('cost'))) fig = plt.figure() plt.plot(tornument_size, costs, label='cost') plt.plot(tornument_size, scores, label='fitness') fig.suptitle('Impact of increasing tornument size', fontsize=12) plt.xlabel('tornument size', fontsize=8) plt.ylabel('value', fontsize=8) fig.savefig('3_genetic_c_3') plt.legend() plt.show()
from OptimizationAlgorithms import genetic as g from problem3 import problem as p import matplotlib.pyplot as plt pr = p.Problem() population_size = [10, 100, 1000] number_of_generations = 20 mutation_rate = 0.02 tornument_size = 4 plot = False costs = [] scores = [] for siz in population_size: sol = g.genetic(pr, siz, number_of_generations, mutation_rate, tornument_size, plot) costs.append(sol.get('cost')) scores.append(sol.get('score')) print('state = {} , score = {} , cost = {} '.format( sol.get('state'), sol.get('score'), sol.get('cost'))) fig = plt.figure() plt.plot(population_size, costs, label='cost') plt.plot(population_size, scores, label='fitness') fig.suptitle('Impact of increasing population size', fontsize=12) plt.xlabel('population size', fontsize=8) plt.ylabel('value', fontsize=8) fig.savefig('3_genetic_b') plt.legend() plt.show()
from OptimizationAlgorithms import genetic as g from problem3 import problem as p import matplotlib.pyplot as plt pr = p.Problem() population_size = 100 number_of_generations = [50, 500, 5000] mutation_rate = 0.02 tornument_size = 4 plot = False costs = [] scores = [] for number in number_of_generations: sol = g.genetic(pr, population_size, number, mutation_rate, tornument_size, plot) costs.append(sol.get('cost')) scores.append(sol.get('score')) print('state = {} , score = {} , cost = {} '.format( sol.get('state'), sol.get('score'), sol.get('cost'))) fig = plt.figure() plt.plot(number_of_generations, costs, label='cost') plt.plot(number_of_generations, scores, label='fitness') fig.suptitle('Impact of increasing number of generations', fontsize=12) plt.xlabel('number of generations', fontsize=8) plt.ylabel('value', fontsize=8) fig.savefig('3_genetic_a') plt.legend() plt.show()
from OptimizationAlgorithms import genetic as g from problem2 import problem as p pr = p.Problem() population_size = 100 number_of_generations = 20 mutation_rate = 0.02 tornument_size = 4 sol = g.genetic(pr, population_size, number_of_generations, mutation_rate, tornument_size) print('state = {} , score = {} , cost = {} '.format(sol.get('state'), sol.get('score'), sol.get('cost')))