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 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