84 lines
3.4 KiB
Python
84 lines
3.4 KiB
Python
import array
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import numpy as np
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import random
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import time
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import sys
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from deap import algorithms
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from deap import base
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from deap import creator
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from deap import tools
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from genetic_operators import GeneticOperators
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def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, maxtime, maxgen, halloffame, compute_perf, perf_metric):
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# Evaluate the individuals with an invalid fitness
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invalid_ind = [ind for ind in population if not ind.fitness.valid]
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fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
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for ind, fit in zip(invalid_ind, fitnesses):
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ind.fitness.values = fit
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if halloffame is not None:
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halloffame.update(population)
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# Begin the generational process
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gen = 0
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maxtime = time.strptime(maxtime, '%Mm%Ss')
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maxtime = maxtime.tm_min*60 + maxtime.tm_sec
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start_time = time.time()
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while time.time() - start_time < maxtime and gen < maxgen:
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# Vary the population
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offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
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# Evaluate the individuals with an invalid fitness
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invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
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fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
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for ind, fit in zip(invalid_ind, fitnesses):
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ind.fitness.values = fit
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# Update the hall of fame with the generated individuals
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if halloffame is not None:
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halloffame.update(offspring)
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# Select the next generation population
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population[:] = toolbox.select(population + offspring, mu)
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# Update the statistics with the new population
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gen = gen + 1
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best_profile = '(%s)'%','.join(map(str,halloffame[0]));
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best_performance = compute_perf(halloffame[0].fitness.values[0])
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sys.stdout.write('Generation %d | Time %d | Best %d %s [ for %s ]\n'%(gen, time.time() - start_time, best_performance, perf_metric, best_profile))
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sys.stdout.write('\n')
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return population
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def genetic(statement, context, TemplateType, build_template, parameter_names, all_parameters, compute_perf, perf_metric, out):
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gen = GeneticOperators(context.devices[0], statement, all_parameters, parameter_names, TemplateType, build_template)
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMin)
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toolbox = base.Toolbox()
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toolbox.register("individual", tools.initIterate, creator.Individual, gen.init)
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("evaluate", gen.evaluate)
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toolbox.register("mate", tools.cxTwoPoint)
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toolbox.decorate("mate", gen.repair)
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toolbox.register("mutate", gen.mutate)
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toolbox.decorate("mutate", gen.repair)
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toolbox.register("select", tools.selNSGA2)
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pop = toolbox.population(n=30)
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hof = tools.HallOfFame(1)
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best_performer = lambda x: max([compute_perf(hof[0].fitness.values[0]) for t in x])
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best_profile = lambda x: '(%s)'%','.join(map(str,hof[0]))
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cxpb = 0.5
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mutpb = 0.2
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("max (" + perf_metric + ")", lambda x: max([compute_perf(hof[0].fitness.values[0]) for t in x]))
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stats.register("profile ", lambda x: '(%s)'%','.join(map(str,hof[0])))
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pop = eaMuPlusLambda(pop, toolbox, 30, 50, cxpb=0.2, mutpb=0.3, maxtime='5m0s', maxgen=200, halloffame=hof, compute_perf=compute_perf, perf_metric=perf_metric)
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