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single_obj_evolution.py
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single_obj_evolution.py
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'''
Initialized on 2016/12/16
Josh Lopez-Binder. hi.
copied from
https://github.com/DEAP/deap/blob/a90d3d599aa789a0083f5bc299803ec32d491cbd/examples/gp/symbreg.py
'''
from __future__ import print_function
import sys, imp
import deap.gp as gp
import pygraphviz as pgv
import numpy as np
import time
#import vector_operations
import math, random, operator
import uuid
import grower
import vector_operations
import brain
imp.reload(grower)
imp.reload(vector_operations)
imp.reload(brain)
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
from deap import gp
''' create pset '''
pset = brain.big_pset()
''' create fitnessMin and Individual '''
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax)
''' toolbox '''
toolbox = base.Toolbox()
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=5)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("compile", gp.compile, pset=pset)
def make_phenotype(genome):
random.seed(81) #deterministic
runner = grower.FixedFootprintGrower()
func = toolbox.compile(expr=genome)
seed = grower.seed_stem(func)
phenotype = runner.grow(seed,t_steps=20)
random.seed()
return phenotype
''' fitness evaulator '''
def evalPhenotype(genome):
'''
Note: must return a tuple value!
'''
phenotype = make_phenotype(genome)
health_scores = phenotype.get_health_scores()
return np.sum(health_scores),
#############################################################
## PARAMETERS
POP_SIZE = 100
print("pop: {}".format(POP_SIZE))
N_GEN = 50
print("ngen: {}".format(N_GEN))
max_size = 17 #of processor tree
prob_mate = 0.5
prob_mutate = 0.1
toolbox.register("evaluate", evalPhenotype)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("mate", gp.cxOnePoint)
toolbox.register("expr_mut", gp.genHalfAndHalf, min_=1, max_=7)
toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=max_size))
toolbox.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=max_size))
''' Add Genealogy recorder '''
history = tools.History()
toolbox.decorate("mate", history.decorator)
toolbox.decorate("mutate",history.decorator)
class EvolutionStuff(object):
''' organizes stuff from evolution '''
def __init__(self,final_pop,logbook,hall_of_fame,history,toolbox,pset):
self.final_pop = final_pop
self.logbook = logbook
self.hall_of_fame = hall_of_fame
self.history = history
self.toolbox = toolbox
self.pset = pset
def ea_simple(population, toolbox, cxpb, mutpb, ngen, stats=None, hallofffame=None, verbose=__debug__):
logbook = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
# Select the next generation individuals
offspring = toolbox.select(population, len(population))
# Vary the pool of individuals
offspring = varAnd(offspring, toolbox, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Replace the current population by the offspring
population[:] = offspring
# Append the current generation statistics to the logbook
record = stats.compile(population) if stats else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print( logbook.stream)
return population, logbook
def main():
random.seed()
pop = toolbox.population(n=POP_SIZE)
history.update(pop)
hof = tools.HallOfFame(1)
stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
stats_size = tools.Statistics(len)
mstats = tools.MultiStatistics(fitness=stats_fit,size=stats_size)
stats_fit.register("avg", np.mean)
stats_fit.register("std", np.std)
stats_fit.register("min", np.min)
stats_fit.register("max", np.max)
pop, log = algorithms.eaSimple(pop, toolbox, prob_mate, prob_mutate, N_GEN, stats=stats_fit,
halloffame=hof, verbose=True)
# print log
stuff = EvolutionStuff(pop, log, hof, history, toolbox, pset)
return stuff
if __name__ == "__main__":
main()