## Evolutionary Demo
## Doug Blank, Emergence 2006
## edited to adjust fitness function to account for correct characters in a row

from pyrobot.brain.ga import *

trials = 15     #KM
old = 0         #KM
new = 0         #KM
phrase = "evolutionary systems are great methods to search a space"
size = len(phrase)

class PhraseGAold(GA):
    def fitnessFunction(self, i):
        sum = 0
        for c in range(len(self.pop.individuals[i].genotype)):
            if self.pop.individuals[i].genotype[c] == phrase[c]:
               sum += 1
        return float(sum) / len(self.pop.individuals[i].genotype)
    def isDone(self):
        print "Best:",
        self.pop.bestMember.display()
        return (phrase == string.join(self.pop.bestMember.genotype, ""))

class PhraseGAnew(GA):
    def fitnessFunction(self, i):
        sum = 0
        inrow = 0     #KM - number in a row
        for c in range(len(self.pop.individuals[i].genotype)):
            if self.pop.individuals[i].genotype[c] == phrase[c]:
                inrow += 1     #KM
                sum += inrow   #KM
            else:              #KM
                inrow = 0      #KM
        return float(sum) / len(self.pop.individuals[i].genotype)
    def isDone(self):
        print "Best:",
        self.pop.bestMember.display()
        return (phrase == string.join(self.pop.bestMember.genotype, ""))

# loop once for each trial, testing old & new, and keeping for each a
# running total of the number of generations required to find phrase
for i in range (trials):
    ga1 = PhraseGAold(Population(300, Gene, size=size, mode='char',
                                verbose=1, elitePercent = .1,
                                crossoverPoints = 2),
                     mutationRate=0.06, crossoverRate=0.6, verbose=1,
                     maxGeneration=0)
    ga1.evolve()
    old += ga1.generation
    ga2 = PhraseGAnew(Population(300, Gene, size=size, mode='char',
                                 verbose=1, elitePercent = .1,
                                 crossoverPoints = 2),
                      mutationRate=0.06, crossoverRate=0.6, verbose=1,
                      maxGeneration=0)
    ga2.evolve()
    new += ga2.generation

print "Average generations out of " + str(trials) +  " trial runs"
print "Old version: " + str(float(old / trials))
print "New version: " + str(float(new / trials))