stupidpythonprojects/evolutionary_algorithm/ea.py

125 lines
4.8 KiB
Python

#!/usr/bin/env python3
class Individual(object):
def __init__(self, fitnessFunction, mutationFunction, inheritanceFunction, parents, initialGenome=None):
'''
:param fitnessFunction: lambda with fitness function f:R^L → R
:param mutationFunction: lambda with mutation function m:R^L → R^L
:param parents: list of individuals used as parents
:param initialGenome: if List of parents is None, this genome is used for initialization
'''
self.genome = []
self.fitnessFunction = fitnessFunction
self.parents = parents
self.mutationFunction = mutationFunction
self.inheritanceFunction = inheritanceFunction
if parents is None:
self.genome = initialGenome
else:
self.inheritate()
def inheritate(self):
'''
only one parent, just copy genome
:return: None
'''
self.genome = self.parents[0].genome
def mutate(self):
'''
juast apply the given mutation function to out genom
:return:
'''
self.genome = self.mutationFunction(self.genome)
def evaluateFitness(self):
'''
apply fitness function for fintness evaluation
:return: fitness value
'''
return self.fitnessFunction(self.genome)
class EvolutionaryPopulation(object):
def __init__(self, # dummy default values:
L = 3, # genome of length 3
offspringSize = 2, # offspring's size
fitnessFunction=lambda genome: 0, # dummy fitness function
inheritanceFunction=lambda parents: parents[0].genome, # copy genome from first parent
mutationFunction=lambda genome: genome, # no mutation
externalSelectionFunction=lambda fitness: list(range(len(fitness))), # keep whole population
parentSelectionFunction=lambda population, fitness: list(range(len(population))) # all individuals are parents
):
self.L = L
self.fitnessFunction = fitnessFunction
self.inheritanceFunction = inheritanceFunction
self.mutationFunction = mutationFunction
self.externalSelectionFunction = externalSelectionFunction
self.parentSelectionFunction = parentSelectionFunction
self.offspringSize = offspringSize
self.population = []
self.fitness = []
self.generation = 0
def addIndividual(self, genome):
self.population.append(Individual(self.fitnessFunction, self.mutationFunction, self.inheritanceFunction, None, genome))
def evaluateFitness(self):
self.fitness = []
for individual in self.population:
self.fitness.append(individual.evaluateFitness())
def externalSelection(self):
# externalSelectionFunction returns indices of individuals to keep:
toKeep = self.externalSelectionFunction(self.fitness)
oldPopulation = self.population
self.population = []
for i in range(len(oldPopulation)):
if i in toKeep:
self.population.append(oldPopulation[i])
def generateOffspring(self):
self.evaluateFitness()
for i in range(self.offspringSize):
parentIndices = self.parentSelectionFunction(self.population, self.fitness)
parents = []
for pI in parentIndices:
parents.append(self.population[pI])
newIndividual = Individual(self.fitnessFunction, self.mutationFunction, self.inheritanceFunction, parents)
newIndividual.mutate()
self.population.append(newIndividual)
# update fitness:
self.fitness.append(newIndividual.evaluateFitness())
def printPopulation(self):
# printPopulation sorted by fitness:
# (use a dictionary for easy sorting)
print("\nGeneration " + str(self.generation) + ":")
d = {}
for i in range(len(self.population)):
d[self.fitness[i]] = self.population[i].genome
for key in d.keys():
print("fitness: " + str(key) + ", \t\tgenome: " + str(d[key]))
def performCycle(self, numCycles = 1):
if self.generation == 0:
self.externalSelection()
for i in range(numCycles):
self.generateOffspring()
self.printPopulation()
self.externalSelection()
self.generation += 1