paul.py

from genetic import *

class myIndividual(Individual):

    # Gene:     Codifica um simples parametro
    # Alelos:   Valores que o gene pode assumir
    # Exemplo: Gene represetando cor de um objeto pode ter alelos como azul, preto, amerelo

    # Genes do passive walker, cada posicao contem um range que representa os possiveis 
    # valores do gene(alelos)
    alleles = [(600,600), (200,600), (10, 100), (10, 100), (1, 60), (0, 2*pi), (0, 2*pi), (0, 2*pi), (0, 2*pi)]
    length = 9

    """ Passive walker genes                                        alleles values range based in 600x600 resolution
        pos_w -- the initial position           (fixed)             x-position 537 to all chromosomes
        pos_h -- the initial position                               y-position alleles 0 to 400
        ul -- the length of the upper leg                           10 to 200
        ll -- the length of the lower leg                           10 to 200
        w -- the width of the robot                                 1 to 60
        lua -- the angle of the left hip                            0 to 360 degrees
        lla -- the angle of the left ankle                          0 to 360 degrees
        rua -- the angle of the right hip                           0 to 360 degrees
        rla -- the angle of the right ankle                         0 to 360 degrees
    """

    def __init__(self, chromosome=None):
        self.chromosome = chromosome or self._makechromosome()
        self.score = None  # set during evaluation

    def _makechromosome(self):
        return [random.uniform(self.alleles[gene][0], self.alleles[gene][1]) for gene in range(self.length)]

    # sample mutation method
    def _pick(self, gene):
        "chooses a random allele to replace this gene's allele."
        self.chromosome[gene] = random.uniform(self.alleles[gene][0], self.alleles[gene][1])


    def evaluate(self, generation=0, indiv=0, show_sim=False, optimum=None):
        "Evaluate individual fitness score."

        if indiv%10 == 0 and show_sim==True:                       # apresenta a simulacao grafica a cada 10 individuos no processo de avaliacao
            s = simulation(show=True)
        else:
            s = simulation(show=False)

        walk_time, distancia = s.individual_sim((self.chromosome[0], self.chromosome[1]), self.chromosome[2], self.chromosome[3], self.chromosome[4], self.chromosome[5], self.chromosome[6], self.chromosome[7], self.chromosome[8], generation, indiv)

        #self.score = score
        self.score = 0.9*walk_time + 0.3*distancia

        return 0

    def __repr__(self):
        "returns string representation of self"
        chromosome_str = ''
        for gene in range (len(self.chromosome)):
            chromosome_str += ' ' + str(self.chromosome[gene])

        return '<%s chromosome="%s" \nscore=%s>' % \
               (self.__class__.__name__,
                chromosome_str, self.score)

g = Environment(myIndividual, size=200, maxgenerations=200, crossover_rate=0.90, mutation_rate=0.3, optimum=100)
g.run()