pop_size = int(arguments['<pop_size>'])
solutions = generate_population(N, pop_size)
Pop = Population(N, solutions, XOVER_PROB, MUTATION_PROB)
optimal_solutions = []
iterations = 0
# Genetic algorithm loop starts here
while iterations < MAX_ITER:
print_iterations = 'Iteration=%i' % iterations
Printer(print_iterations)
# create the next generation
next_generation = Pop.build_new_population()
# modify the current population to the new one
Pop.regenerate_population(next_generation)
# increment generations
iterations += 1
outfile_stats = 'output/N%iP%i_%s.txt' % (N, len(solutions), timestamp)
outfile_sols = 'output/solutions_N%iP%i_%s.txt' % (N, len(solutions), timestamp)
Pop.print_stats()
if Pop.optimal_solutions:
print 'Found %i solutions' % len(Pop.optimal_solutions)
for s in Pop.optimal_solutions:
print s
Pop.export_stats(outfile_stats)
Pop.export_optimal_solutions(outfile_sols)
