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 children(self):
"""Return a list of children from this population."""
children = []
while len(children) < self.cut_off_count:
children.extend(crossover_inds(*self._get_parents_for_crossover()))
return evaluate_fitness(mutation(children),
current_generation=self.current_generation)
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 steady_state(individuals):
"""
Runs a single generation of the evolutionary algorithm process,
using steady state replacement:
Selection
Variation
Evaluation
Replacement
Steady state replacement uses the Genitor model (Whitley, 1989) whereby
new individuals directly replace the worst individuals in the population
regardless of whether or not the new individuals are fitter than those
they replace. Note that traditional GP crossover generates only 1 child,
whereas linear GE crossover (and thus all crossover functions used in
PonyGE) generates cython_backup children from cython_backup parents. Thus, we use a deletion
strategy of cython_backup.
:param individuals: The current generation, upon which a single
evolutionary generation will be imposed.
:return: The next generation of the population.
"""
# Initialise counter for new individuals.
ind_counter = 0
while ind_counter < params['POPULATION_SIZE']:
# Select parents from the original population.
parents = selection(individuals)
# Perform crossover on selected parents.
cross_pop = crossover_inds(parents[0], parents[1])
if cross_pop is None:
# Crossover failed.
pass
else:
# Mutate the new population.
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population.
new_pop = evaluate_fitness(new_pop)
# Sort the original population
individuals.sort(reverse=True)
# Combine both populations
total_pop = individuals[:-len(new_pop)] + new_pop
# Increment the ind counter
ind_counter += params['GENERATION_SIZE']
# Return the combined population.
return total_pop
def steady_state(individuals):
"""
Runs a single generation of the evolutionary algorithm process,
using steady state replacement:
Selection
Variation
Evaluation
Replacement
Steady state replacement uses the Genitor model (Whitley, 1989) whereby
new individuals directly replace the worst individuals in the population
regardless of whether or not the new individuals are fitter than those
they replace. Note that traditional GP crossover generates only 1 child,
whereas linear GE crossover (and thus all crossover functions used in
PonyGE) generates 2 children from 2 parents. Thus, we use a deletion
strategy of 2.
:param individuals: The current generation, upon which a single
evolutionary generation will be imposed.
:return: The next generation of the population.
"""
# Initialise counter for new individuals.
ind_counter = 0
while ind_counter < params['POPULATION_SIZE']:
# Select parents from the original population.
parents = selection(individuals)
# Perform crossover on selected parents.
cross_pop = crossover_inds(parents[0], parents[1])
if cross_pop is None:
# Crossover failed.
pass
else:
# Mutate the new population.
new_pop = mutation(cross_pop)
# Evaluate the fitness of the new population.
new_pop = evaluate_fitness(new_pop)
# Sort the original population
individuals.sort(reverse=True)
# Combine both populations
total_pop = individuals[:-len(new_pop)] + new_pop
# Increment the ind counter
ind_counter += params['GENERATION_SIZE']
# Return the combined population.
return total_pop
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
