def optimisation(N=80, G=100, p_c=0.7, p_m=0.001, log=False, gui=False):
fit_eval = FitnessEvaluator()
if log:
print('Performance optimisation')
print('------------------------')
print('Genetic algorithm:')
print('Population: N = ', N)
print('Generations: G = ', G)
print('Crossover probability: p_c = ', p_c)
print('Mutation probability: p_m = ', p_m)
if gui:
p_graph.init('Performance profile',
f'p_c = {p_c} p_m = {p_m} N = {N} G = {G}')
# Generates random population
perf_run = GeneticRun(gc.length(gc.default_gene_format), N)
if log:
print('\nGenerated initial population')
print('Generation: 0\n')
# Evolution loop
while perf_run.generation < G:
perf_run.evolve(fit_eval.performance, p_c, p_m)
# if log:
# print(f'Generation: {perf_run.generation}')
# print(f'Average fitness: {perf_run.record[-1]["avg_fitness"]}')
# print(f'Max fitness: {perf_run.record[-1]["max_fitness"]}\n')
if gui:
p_graph.update(perf_run.record)
# Present best specimen so far
best_specimen_ch = max(perf_run.record,
key=lambda x: x['max_fitness'])['fittest_specimen']
best_specimen = gc.decode(best_specimen_ch)
distance, fuel, speed = fit_eval.simulate(best_specimen_ch, tries=10)
if log:
print('-------------')
print('Best specimen')
print('-------------')
print('Best chromosones:\n', best_specimen_ch)
print('Best parameters:')
pp.pprint(best_specimen)
print('Specimen performance:')
print(' Distance (m):', distance)
print(' Fuel use (l):', fuel)
print(' Speed (km/h):', speed)
if gui:
p_graph.join()
# Return best specimen
return best_specimen
