If you are used to any other evolutionary algorithm framework, you’ll notice we do things differently with DEAP. Instead of limiting you with predefined types, we provide ways of creating the appropriate ones. Instead of providing closed initializers, we enable you to customize them as you wish. Instead of suggesting unfit operators, we explicitly ask you to choose them wisely. Instead of implementing many sealed algorithms, we allow you to write the ones that fit all your needs. This tutorial will present a quick overview of what DEAP is all about along with what every DEAP program is made of.
The first thing to do is to think of the appropriate type for your problem.
Then, instead of looking in the list of available types, DEAP enables you to
build your own. This is done with
creator module. Creating an appropriate type might seem
overwhelming but the creator makes it very easy. In fact, this is usually done
in a single line. For example, the following creates a
for a minimization problem and an
Individual class that is derived
from a list with a fitness attribute set to the just created fitness.
from deap import base, creator creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin)
That’s it. More on creating types can be found in the Creating Types tutorial.
Once the types are created you need to fill them with sometimes random values,
sometime guessed ones. Again, DEAP provides an easy mechanism to do just that.
Toolbox is a container for tools of all sorts
including initializers that can do what is needed of them. The following takes
on the last lines of code to create the initializers for individuals
containing random floating point numbers and for a population that contains
import random from deap import tools IND_SIZE = 10 toolbox = base.Toolbox() toolbox.register("attribute", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=IND_SIZE) toolbox.register("population", tools.initRepeat, list, toolbox.individual)
This creates functions to initialize populations from individuals that are
themselves initialized with random float numbers. The functions are registered
in the toolbox with their default arguments under the given name. For example,
it will be possible to call the function
instantly create a population.
More initialization methods
are found in the Creating Types tutorial and the various
Operators are just like initializers, except that some are already
implemented in the
tools module. Once you’ve chosen the perfect
ones, simply register them in the toolbox. In addition you must create your
evaluation function. This is how it is done in DEAP.
def evaluate(individual): return sum(individual), toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evaluate)
The registered functions are renamed by the toolbox, allowing generic algorithms that do not depend on operator names. Note also that fitness values must be iterable, that is why we return a tuple in the evaluate function. More on this in the Operators and Algorithms tutorial and Examples.
Now that everything is ready, we can start to write our own algorithm. It is usually done in a main function. For the purpose of completeness we will develop the complete generational algorithm.
def main(): pop = toolbox.population(n=50) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 # Evaluate the entire population fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit for g in range(NGEN): # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals offspring = map(toolbox.clone, offspring) # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offspring pop[:] = offspring return pop
It is also possible to use one of the four algorithms readily
available in the
algorithms module, or build from some building
blocks called variations also available in this module.