class EA(object):
"""This class is the entry point to the execution of the algorithm.
Attributes:
minfun (function): Function used to calculate the fitness of a genome.
bounds (list): Contains the minimum and maximum values that each variable can take from a candidate
solution.
population (Population): Object which contains a list of genomes.
best_genome (Genome): Optimal solution calculated by the algorithm.
Args:
minfun_ (function): The function to be set.
bounds_ (list): List of values to be set.
p_size (int): Maximum size of our population.
"""
def __init__(self, minfun_, bounds_, p_size):
self.minfun = minfun_
self.bounds = bounds_
self.population = Population(minfun_, bounds_, p_size)
self.best_genome = None
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)
# print(f'After:\n {self.population}\n')
# self.best()
# print(f'Best Genome after: {self.best_genome.array}, fitness={self.best_genome.fitness} ')
def best(self):
"""Gets the best genome
Returns:
best_genome (Genome): Optimal solution calculated by the algorithm.
"""
self.population.ascendent_sort()
self.best_genome = self.population.collection[0]
return self.best_genome
