def average_n_runs(simulation, n, generations):
simulation['stop'].update({'fitness': None, 'generation': generations})
datasets = [run_simulation(simulation, log=False) for _ in range(n)]
sum_dataset = {
'average_fitnesses': [0 for _ in range(generations)],
'sigmas': [0 for _ in range(generations)],
'best_fitnesses': [0 for _ in range(generations)]
}
for i in range(generations):
for dataset in datasets:
for key in sum_dataset.keys():
sum_dataset[key][i] += dataset[key][i]
avg_func = lambda x: float(x) / n
avg_dataset = {
'generation_number': generations,
'simulation': simulation,
'average_fitnesses': list(map(avg_func, sum_dataset['average_fitnesses'])),
'sigmas': list(map(avg_func, sum_dataset['sigmas'])),
'best_fitnesses': list(map(avg_func, sum_dataset['best_fitnesses']))
}
return avg_dataset
def average_n_runs(simulation, n, generations):
simulation['stop'].update({'fitness': None, 'generation': generations})
datasets = [run_simulation(simulation, log=False) for _ in range(n)]
sum_dataset = {
'average_fitnesses': [0 for _ in range(generations)],
'sigmas': [0 for _ in range(generations)],
'best_fitnesses': [0 for _ in range(generations)]
}
for i in range(generations):
for dataset in datasets:
for key in sum_dataset.keys():
sum_dataset[key][i] += dataset[key][i]
avg_func = lambda x: float(x) / n
avg_dataset = {
'generation_number': generations,
'simulation': simulation,
'average_fitnesses':
list(map(avg_func, sum_dataset['average_fitnesses'])),
'sigmas': list(map(avg_func, sum_dataset['sigmas'])),
'best_fitnesses': list(map(avg_func, sum_dataset['best_fitnesses']))
}
return avg_dataset
def simulate_n_times(simulation, n):
results = [run_simulation(simulation, log=False) for _ in range(n)]
print(simulation['problem_parameters']['population_size'],
sum(r['generation_number'] < 100 for r in results))
return {
'data': list((r['generation_number'] for r in results)),
'label': simulation['problem_parameters']['population_size']
}
def simulate_n_times(simulation, n):
results = [run_simulation(simulation, log=False) for _ in range(n)]
print(
simulation['problem_parameters']['population_size'],
sum(r['generation_number'] < 100 for r in results)
)
return {
'data': list((r['generation_number'] for r in results)),
'label': simulation['problem_parameters']['population_size']
}
},
'adult_selection_method': over_production,
'mate_selection_method': sigma_scaled,
'crossover_method': splice,
'crossover_rate': 0.9,
'mutation_method': mutate_string_genome,
'mutation_rate': 0.01,
'stop': {
'fitness': 1.0,
'generation': 1000
},
}
STOP_GENERATION = 1000
while True:
L = max(int(1.1 * L), L + 1)
simulation['problem_parameters'].update({'genome_size': L})
r = run_simulation(simulation, log=False)
if r['generation_number'] < STOP_GENERATION:
best = "found in {} generations".format(r['generation_number'])
best += '\n'
best += str(
surprising_sequences.phenotype_representation(
r['final_best_individual']['phenotype'],
char_set_size=S,
genome_size=L))
best += '\n'
print(best)
else:
break
}
S = 37
LOCAL = False
print('LOCAL={}'.format(LOCAL))
print("S={}".format(S))
L = 84
stops = 0
while True:
s['problem_parameters'].update({
'char_set_size': S,
'genome_size': L,
'local': LOCAL
})
r = run_simulation(s, log=False)
if r['generation_number'] < 1000:
print(r['generation_number'], '\t', surprising_sequences.phenotype_representation(
r['final_best_individual']['phenotype'],
char_set_size=S,
genome_size=L)
)
L = max(int(L * 1.1), L + 1)
# L += 1
stops = 0
else:
stops += 1
print('1000 stop')
if stops == 3:
break
'adult_selection_method': full_generational_replacement,
'mate_selection_method': ranked,
'crossover_method': splice,
'crossover_rate': 0.25,
'mutation_method': mutate_string_genome,
'mutation_rate': 0.01,
'stop': {
'fitness': 1.0,
'generation': None
},
'plot_sigmas': False
}
SIMULATIONS = [
ONE_MAX,
LOLZ,
LOCALLY_SURPRISING,
GLOBALLY_SURPRISING
]
if __name__ == "__main__":
for simulation in SIMULATIONS:
results = run_simulation(simulation)
plot_simulation_results(results)
