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copied!<p>Hi after reviewing the documentation of Pyevolve there doesn't seem to be any way to resume an evolution base on what you stored in the database (strange behaviour).</p> <p>If you want to implement this type of mechanism, you could look at pickling your population once and a while and implementing the whole thing in Pyevolve.</p> <p>Or, you could try <a href="http://deap.googlecode.com" rel="nofollow">DEAP</a> a very open framework that let you see and manipulate every aspect of an evolutionary algorithm, transparently. And there is already some <em>checkpointing</em> mechanism implemented.</p> <p>Here is what your code would look like in DEAP.</p> <pre><code>import random from deap import algorithms, base, creator, tools # Create the needed types creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) # Container for the evolutionary tools toolbox = base.Toolbox() toolbox.register("attr", random.random, 1, 15) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr, 6) toolbox.register("population", tools.initRepeat, list, toolbox.individual) # Operator registering toolbox.register("evaluate", eval_func) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) population = toolbox.population(n=10) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("Max", max) checkpoint = tools.Checkpoint(population=population) GEN, CXPB, MUTPB = 0, 0.5, 0.1 while stats.Max() < CONDITION: # Apply standard variation (crossover followed by mutation) offspring = algorithms.varSimple(toolbox, population, cxpb=CXPB, mutpb=MUTPB) # Evaluate the individuals fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit # Select the fittest individuals offspring = [toolbox.clone(ind) for ind in toolbox.select(offspring, len(offspring)] # The "[:]" is important to not replace the label but what it contains population[:] = offspring stats.update(population) if GEN % 20 == 0: checkpoint.dump("my_checkpoint") GEN += 1 </code></pre> <p>Note that the above code has not been tested. But it does everything you request for. Now how to load a checkpoint and restart an evolution.</p> <pre><code>checkpoint = tools.Checkpoint() checkpoint.load("my_checkpoint.ems") population = checkpoint["population"] # Continue the evolution has in before </code></pre> <p>Moreover, DEAP is very well documented and has over 25 diversified examples that help new user to ramp up very quickly, I also heard that developers answer to question very quickly.</p>

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