def single_fit(g, initial_num=42):
"""Returns the fitness given a specific gene."""
return float((1.0 / (1.0 + abs(initial_num - gene.evaluate(g)))))
def generate_fitness(gene_array, initial_num=42):
"""Generates a list of fitnesses for a given array of genes."""
return [
float((0.00001 / (0.00001 + abs(initial_num - gene.evaluate(g)))))
for g in gene_array
]
def generate_fitness(gene_array, initial_num=42):
"""Generates a list of fitnesses for a given array of genes."""
return [float((0.00001 / (0.00001 +
abs(initial_num - gene.evaluate(g)))))
for g in gene_array]
def single_fit(g, initial_num=42):
"""Returns the fitness given a specific gene."""
return float((1.0 / (1.0 +
abs(initial_num - gene.evaluate(g)))))
# Keep track of the previous and max fit to prevent cluttering of the console.
prev_gene = ""
while not gene_pool[-1][1] == 1:
# When the Max fitted gene pool is 1, this is when a solution is found.
# ALWAYS write to the file, but don't print to stdout
f.write("\nGENERATION: %d\n" % (generation))
for g, fit in gene_pool:
f.write("%s %1.8f %s\n" % (g, fit, gene.show_eq(g)))
# If there is a new maximum, do the verbose output
if not (prev_gene == gene_pool[-1][0]):
os.system('clear')
print "GENERATION: %d\n" % (generation)
print "Best Gene\n\t - Fitness: %1.5f\n" % (gene_pool[-1][1])
print "\t - Value: %d\n" % (gene.evaluate(gene_pool[-1][0]))
for g, fit in gene_pool:
print "%s %1.8f %s" % (g, fit, gene.show_eq(g))
# Reproduce 2 genes and add the resulting ones to the list of genes
ng1, ng2 = manage.reproduce(gene_pool, REPRODUCTION_RATE, MUTATION_RATE)
manage.add(gene_pool, ng1, FINAL_NUMBER, MAX_GENE_POOL)
manage.add(gene_pool, ng2, FINAL_NUMBER, MAX_GENE_POOL)
generation += 1
prev_gene = gene_pool[-1][1]
# Pretty print things when the genes have solved the problem
f.write("\nGENERATION: %d\n" % (generation))
for g, fit in gene_pool:
