def search_loop():
"""
This is a standard search process for an evolutionary algorithm. Loop over
a given number of generations.
:return: The final population after the evolutionary process has run for
the specified number of generations.
"""
# Initialise population
individuals = initialisation(params['POPULATION_SIZE'])
# Evaluate initial population
individuals = evaluate_fitness(individuals)
# Generate statistics for run so far
get_stats(individuals)
# Traditional GE
for generation in range(1, (params['GENERATIONS'] + 1)):
stats['gen'] = generation
# New generation
individuals = params['STEP'](individuals)
return individuals
def search_loop():
"""
This is a standard search process for an evolutionary algorithm. Loop over
a given number of generations.
:return: The final population after the evolutionary process has run for
the specified number of generations.
"""
if params['MULTICORE']:
# initialize pool once, if mutlicore is enabled
params['POOL'] = Pool(processes=params['CORES'], initializer=pool_init,
initargs=(params,)) # , maxtasksperchild=1)
# Initialise population
individuals = initialisation(params['POPULATION_SIZE'])
# Evaluate initial population
individuals = evaluate_fitness(individuals)
# Generate statistics for run so far
get_stats(individuals)
# Traditional GE
for generation in range(1, (params['GENERATIONS']+1)):
stats['gen'] = generation
# New generation
individuals = params['STEP'](individuals)
if params['MULTICORE']:
# Close the workers pool (otherwise they'll live on forever).
params['POOL'].close()
return individuals
def __init__(self, selection_proportion):
self.current_generation = 0
self.individuals = sorted(
evaluate_fitness(initialisation(params['POPULATION_SIZE']),
current_generation=self.current_generation))
self.cut_off_count = int(len(self) * (1 - selection_proportion))
self.update_fittest_individuals()
self.update_probabilities()
def __init__(self, ip):
# Interaction probability received in constructor
self.interaction_probability = ip
# Only initialize single individual. Single agent can only have single genetic information
self.individual = initialisation(1)
# Evaluate the fitness for the the individual
self.individual = evaluate_fitness(self.individual)
# Flag which store the boolean value for other neighbouring agents found or not
self.agents_found = False
def __init__(self, ip):
# Interaction probability received in constructor
self.interaction_probability = ip
# Only initialize singel individual. Single agent can only have single genetic information
self.individual = initialisation(1)
# Evaluate the fitness for the the individual
self.individual = evaluate_fitness(self.individual)
# Flag which store the boolean value for other nebouring agents found or not
self.agents_found = False
def search_loop():
"""
This is a standard search process for an evolutionary algorithm. Loop over
a given number of generations.
:return: The final population after the evolutionary process has run for
the specified number of generations.
"""
logf = open(params['FILE_PATH'] + "/log.csv", 'w') #190312: log
set_M() #190307: our mod for learning multiplier
if params['MULTICORE']:
# initialize pool once, if mutlicore is enabled
params['POOL'] = Pool(processes=params['CORES'],
initializer=pool_init,
initargs=(params, )) # , maxtasksperchild=1)
# Initialise population
individuals = initialisation(params['POPULATION_SIZE'])
# Evaluate initial population
individuals = evaluate_fitness(individuals)
# Generate statistics for run so far
get_stats(individuals)
write_log(logf, 0, params['M'], individuals) #190312: log
#write_best(0, params['M'], individuals)
# Traditional GE
for generation in range(1, (params['GENERATIONS'] + 1)):
stats['gen'] = generation
update_M(generation,
individuals) # 190307: our mod for learning multiplier
# New generation
individuals = params['STEP'](individuals)
write_log(logf, generation, params['M'], individuals) #190312: log
#write_best(generation, params['M'], individuals)
print("generation ", generation, "/", params['GENERATIONS'],
" finished at ", datetime.datetime.now()) # 190313: timestamp
if params['MULTICORE']:
# Close the workers pool (otherwise they'll live on forever).
params['POOL'].close()
logf.close()
return individuals
def search_loop():
"""
This is a standard search process for an evolutionary algorithm. Loop over
a given number of generations.
:return: The final population after the evolutionary process has run for
the specified number of generations.
"""
if params['MULTICORE']:
# initialize pool once, if mutlicore is enabled
params['POOL'] = ThreadPool(
processes=params['CORES'],
initializer=pool_init,
initargs=(params, )) # , maxtasksperchild=1)
Logger.log("Generation 0 starts. Initializing...")
# Initialise population
individuals = initialisation(params['POPULATION_SIZE'])
# Evaluate initial population
individuals = evaluate_fitness(individuals)
# Generate statistics for run so far
get_stats(individuals)
# Cleanup after evaluation
if 'cleanup' in dir(params['FITNESS_FUNCTION']):
params['FITNESS_FUNCTION'].cleanup(individuals)
# Traditional GE
for generation in range(1, (params['GENERATIONS'] + 1)):
stats['gen'] = generation
params['CURRENT_EVALUATION'] = 0
params['CURRENT_GENERATION'] = generation
Logger.log("Generation {0} starts...".format(generation))
# New generation
individuals = params['STEP'](individuals)
# Cleanup after evaluation
if 'cleanup' in dir(params['FITNESS_FUNCTION']):
params['FITNESS_FUNCTION'].cleanup(individuals)
Logger.close_all()
if params['MULTICORE']:
# Close the workers pool (otherwise they'll live on forever).
params['POOL'].close()
return individuals
