def act(self):
# Process the information if the agent has sense nearby agents
if self.agents_found:
# Combine the original individual and individuals found by interacting with nearby agents to form
# a population
individuals = self.individual + self.nearby_agents
# Find out parents from the population
parents = selection(individuals)
# Crossover parents and add to the new population.
cross_pop = crossover(parents)
# Mutate the new population.
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population.
new_pop = evaluate_fitness(new_pop)
# Replace the old population with the new population.
individuals = replacement(new_pop, individuals)
# Generate statistics for run so far
get_stats(individuals)
# Sort the individuals list
individuals.sort(reverse=True)
# Get the highest performing individual from the sorted population
self.new_individual = individuals[0]
def step(individuals):
"""
Runs a single generation of the evolutionary algorithm process:
Selection
Variation
Evaluation
Replacement
:param individuals: The current generation, upon which a single
evolutionary generation will be imposed.
:return: The next generation of the population.
"""
# Select parents from the original population.
parents = selection(individuals)
# Crossover parents and add to the new population.
cross_pop = crossover(parents)
# Mutate the new population.
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population.
new_pop = evaluate_fitness(new_pop)
# Replace the old population with the new population.
individuals = replacement(new_pop, individuals)
# Generate statistics for run so far
params['STATISTICS'].get_stats(individuals)
return individuals
def act(self):
# Process the information if the agent has sense nearby agents
if self.agents_found:
# Combine the original individual and individuals found by interacting with nearby agents to form
# a population
individuals = self.individual + self.nearby_agents
# Find out parents from the population
parents = selection(individuals)
# Crossover parents and add to the new population.
cross_pop = crossover(parents)
# Mutate the new population.
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population.
new_pop = evaluate_fitness(new_pop)
# Replace the old population with the new population.
individuals = replacement(new_pop, individuals)
# Generate statistics for run so far
get_stats(individuals)
# Sort the individuals list
individuals.sort(reverse=True)
# Get the higest performing individual from the sorted population
self.new_individual = individuals[0]
def weips_step(individuals, weight_matrix):
"""
Runs a single generation of the evolutionary algorithm process:
Selection
Variation
Evaluation
Replacement
:param individuals: The current generation, upon which a single
:param weight_matrix: The matrix of weights used by the selection method.
:return: The next generation of the population.
"""
# Size of the population
pop_size = params['GENERATION_SIZE']
# Select parents from the original population.
parents = weips_selection(individuals, weight_matrix, pop_size)
# Crossover parents and add to the new population.
cross_pop = crossover(parents)
# Mutate the new population.
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population (Q_t).
new_pop = evaluate_fitness(new_pop)
# Replace the old population with the new population.
individuals = weips_replacement(new_pop, individuals, weight_matrix)
# Generate statistics for run so far
params['STATISTICS'].get_stats(individuals)
return individuals
def step(individuals):
"""
Runs a single generation of the evolutionary algorithm process:
Selection
Variation
Evaluation
Replacement
:param individuals: The current generation, upon which a single
evolutionary generation will be imposed.
:return: The next generation of the population.
"""
# Select parents from the original population.
parents = selection(individuals)
# Crossover parents and add to the new population.
cross_pop = crossover(parents)
# Mutate the new population.
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population.
new_pop = evaluate_fitness(new_pop)
# Replace the old population with the new population.
individuals = replacement(new_pop, individuals)
# Generate statistics for run so far
get_stats(individuals)
return individuals
def step_reload(individuals,initial_population):
individuals.sort(reverse=True)
print(str(individuals[0].fitness) + " -v- " + str(params['RESET_FITNESS']))
if individuals[0].fitness > params['RESET_FITNESS']:
if params['DYNAMIC_ENVIRONMENT_RELOAD_PERCENTAGE']:
print("RESETTING PERCENTAGE OF POP")
new_inds = int(len(initial_population)*params['DYNAMIC_ENVIRONMENT_RELOAD_PERCENTAGE_VALUE'])
#sample from initial pop randomly
reload_pop_a = deepcopy(random.sample(initial_population, new_inds))
reload_pop_b = deepcopy(individuals[:-new_inds])
reload_pop = reload_pop_a + reload_pop_b
reload_pop = evaluation(reload_pop)
reload_pop.sort(reverse=True)
params['RESET_FITNESS'] = reload_pop[0].fitness
print("Reload Threshold now: " + str(params['RESET_FITNESS']))
params['RELOAD_PERFORMED'] = 1
rep_inds = deepcopy(reload_pop)
# If we want to use replacement :D
# individuals = replacement(initial_population, individuals)
else:
print("RESETTING POP")
initial_population = evaluation(initial_population)
initial_population.sort(reverse=True)
params['RESET_FITNESS'] = initial_population[0].fitness
print("Reload Threshold now: "+ str(params['RESET_FITNESS']))
params['RELOAD_PERFORMED'] = 1
rep_inds = deepcopy(initial_population)
#If we want to use replacement :D
#individuals = replacement(initial_population, individuals)
else:
# Select parents
parents = selection(individuals)
# Crossover parents and add to the new population
cross_pop = crossover(parents)
# Mutate the new population
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population
new_pop = evaluation(new_pop)
# Replace the sorted individuals with the new populations
rep_inds = replacement(new_pop, individuals)
params['RELOAD_PERFORMED'] = 0
return rep_inds
def step(individuals):
"""Return individuals and best ever individual from a step of
the EA iteration"""
if params['BASELINE_STEPS']:
individuals = evaluation(individuals)
else:
# Select parents
parents = selection(individuals)
# Crossover parents and add to the new population
cross_pop = crossover(parents)
# Mutate the new population
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population
new_pop = evaluation(new_pop)
# Replace the sorted individuals with the new populations
individuals = replacement(new_pop, individuals)
return individuals