mutation_upper = 1.2 # J.Madge 05.11.2017 Upper limit of the mutation operator.
p = Population()
current_gen = p.gen()
next_gen = current_gen + 1
# J.Madge 06.11.2017 Select the best Resistance and best Spy to represent this generation.
best_res = p.top(1, current_gen, ResGenotype.Type)[0]
best_res.set_index(current_gen)
p.insert(best_res, 'Best')
best_spy = p.top(1, current_gen, SpyGenotype.Type)[0]
best_spy.set_index(current_gen)
p.insert(best_spy, 'Best')
# J.Madge 05.11.2017 Create database for the next generation.
p.create(next_gen)
# J.Madge 05.11.2017 RESISTANCE: Create the next generation.
res_next_gen_genes = []
res_next_gen_genes += p.top(promote, current_gen, ResGenotype.Type)
for i in xrange(tournament_num):
parent1 = p.tournament(tournament_size, current_gen, ResGenotype.Type)
parent2 = p.tournament(tournament_size, current_gen, ResGenotype.Type)
child = parent1.crossover(parent2, res_cross_pos, ResGenotype.Type)
child.mutate(res_mutations, mutation_lower, mutation_upper)
res_next_gen_genes.append(child)
for i, g in enumerate(res_next_gen_genes):
g.reset_data(i)
p.insert(g, next_gen)
from genetic_algorithm import Population
initial_generation = 0 # J.Madge 05.11.2017 Number of the initial generation.
population_size = 20 # J.Madge 05.11.2017 Number of genotypes in the population.
initialise_lower_bound = 0.4 # J.Madge 05.11.2017 Lowest initial values a gene can be initialized to.
initialise_upper_bound = 0.6 # J.Madge 05.11.2017 Highest initial values a gene can be initialized to.
p = Population()
# J.Madge 05.11.2017 Set the population state (size of the population, number of the initial generation).
p.create_state()
p.set_size(population_size)
p.set_gen(initial_generation)
# J.Madge 05.11.2017 Create the initial generation and initialize the contained genotypes to the lower and upper bounds.
p.create(initial_generation)
p.create('Best')
p.initialise(initial_generation, initialise_lower_bound,
initialise_upper_bound)