def find_best_parameters(pop_size, max_gen):
possible_parameters = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
possible_variances = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
best_variance = 0
best_mutation = 0
best_crossover = 0
best_average_elite_solution = 0
populations = initial_populations()
for p_mutation in possible_parameters:
for p_crossover in possible_parameters:
for variance in possible_variances:
print('Trying: ' + str(variance) + ', ' +
str(p_crossover) + ', ' +
str(p_mutation))
elite_solutions = []
for i in range(3):
solution, elite = GA_T(populations[i], pop_size, max_gen,
p_crossover, p_mutation, variance)
elite_solutions.append(bump(elite))
average_elite_solution = mean(elite_solutions)
print('Average elite solution: ' + str(average_elite_solution))
if average_elite_solution > best_average_elite_solution:
print('Found new best values!')
best_average_elite_solution = average_elite_solution
best_variance = variance
best_crossover = p_crossover
best_mutation = p_mutation
print('We found some great parameters!!')
print('Variance: ' + str(best_variance))
print('Probability Crossover: ' + str(best_crossover))
print('Probability Mutation: ' + str(best_mutation))
print('We found some great parameters!!')
print('Variance: ' + str(best_variance))
print('Probability Crossover: ' + str(best_crossover))
print('Probability Mutation: ' + str(best_mutation))
pop_size = 50
max_gen = 200
p_crossover = 0.7
p_mutation = 0.1
variance = 2
elite_solutions = []
elite_solution_vals = []
all_solutions = []
for population in initial_populations():
solution, elite = GA_T(population, pop_size, max_gen,
p_crossover, p_mutation, variance)
elite_solutions.append(elite)
elite_solution_vals.append(bump(elite))
all_solutions.append(solution)
with open("problem3_results.txt", 'w') as output:
output.write('Average: ' + str(mean(elite_solution_vals)))
output.write('\nStandard deviation: ' + str(std(elite_solution_vals)))
best_value = max(elite_solution_vals)
output.write('\nBest value: ' + str(best_value))
output.write('\nBest member: ' +
str(elite_solutions[elite_solution_vals.index(best_value)]))
worst_value = min(elite_solution_vals)
output.write('Worst value: ' + str(worst_value))
