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from scipy import ga
def fitness_ex1(self):
score = 0
desired = ['a','b','c','d','b',-1,.25,.6, .8, 0]
# compare letters
actual = self.get_values()
pairs = zip(actual[:5],desired[:5])
for g,d in pairs:
if g == d:
score += 1
pairs = zip(actual[5:],desired[5:])
for g,d in pairs:
diff = g - d
score -= abs(diff)
return score
def ex1():
""" This example illustrates the main steps in setting
up a genetic optimization:
1. Specify the genes types used to encode your problem
2. Group these genes into a genome.
a. Specify the fitness function that evaluates the genomes.
3. Create a population of the genomes
4. Specify the algorithm used to evolve the population
5.
"""
# 1. First scpecify your genes. To gene types are
# currently supported, list_gene and float_gene
# A list gene chooses its value from a list of values.
# The list can contain any type of object.
g1 = ga.gene.list_gene(['a','b','c','d'])
# Float genes take on a continuous value between two
# bounds.
g2 = ga.gene.float_gene((-1.,1.))
# We'll replicate these genes several times to make a longer
# genome.
all_genes = g1.replicate(5) + g2.replicate(5)
# 2. Create a specialized "list_genome" (as opposed to tree_genome)
# class with the desired fitness function.
# It's structure is defined by our gene list.
class this_genome(ga.genome.list_genome):
pass
this_genome.performance = fitness_ex1
gnm = this_genome(all_genes)
# 3. Create a population of the genomes.
#
pop = ga.population.population(gnm)
# 4. Now use the basic genetic algorithm to evolve the population
#
galg = ga.algorithm.galg(pop)
# change a few settings
settings = {'pop_size':250,'p_replace':.8,'p_cross': .8, 'p_mutate':'gene',
'p_deviation': 0.,'gens':35,'rand_seed':0,'rand_alg':'CMRG'}
galg.settings.update(settings)
galg.evolve()
print galg.pop.best()
if __name__ == '__main__':
ex1()
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