def run(self, iterations):
"""Runs the Differential Evolution algorithm.
Args:
iterations (int): Number of iterations to be made by the algorithm.
"""
# print(f'Before:\n {self.population}\n')
# self.best()
# print(f'Best Genome before: {self.best_genome.array}, fitness={self.best_genome.fitness} ')
mutator = Rand1MutationOperator(self.population, self.bounds, 0.2)
mixer = ExponentialCrossoverOperator(self.minfun)
replacer = ElitistReplacementOperator()
for _ in range(iterations):
candidate_population = Population(None, None, 0)
for target in self.population.collection:
# List with genomes who will be the donors
mutant = mutator.apply(target)
# Genome modified by replacing a few random positions
candidate_genome = mixer.apply(target, mutant)
candidate_population.add(candidate_genome)
# Targets are replaced by candidates from the population if candidate has less fitness than target
self.population = replacer.apply(self.population,
candidate_population)
def apply(self, current_population, candidate_population):
"""
Each genome of 'current_population' is compared with the genome which is in the same position than
'candidate_population' and the one with the best fitness of these two genomes is added to
'result_population'.
Args:
current_population (Population): Object which contains a list of genomes.
candidate_population (list): List which contains a list of genomes.
Returns:
result_population (Population) : Resulting population with the best fitness in each position.
"""
result_population = Population(None, None, 0)
for target, candidate in zip(current_population.collection, candidate_population.collection):
result_population.add(candidate if candidate.fitness < target.fitness else target)
return result_population
