One Max Problem¶
This is the first complete example built with DEAP. It will help new users to
overview some of the framework possibilities. The problem is very simple, we
search for a 1
filled list individual. This problem is widely used in
the evolutionary computation community since it is very simple and it
illustrates well the potential of evolutionary algorithms.
Setting Things Up¶
Here we use the one max problem to show how simple can be an evolutionary algorithm with DEAP. The first thing to do is to elaborate the structures of the algorithm. It is pretty obvious in this case that an individual that can contain a series of booleans is the most interesting kind of structure available. DEAP does not contain any explicit individual structure since it is simply a container of attributes associated with a fitness. Instead, it provides a convenient method for creating types called the creator.
First of all, we need to import some modules.
import random
from deap import base
from deap import creator
from deap import tools
Creator¶
The creator is a class factory that can build at run-time new classes that inherit from a base classe. It is very useful since an individual can be any type of container from list to n-ary tree. The creator allows build complex new structures convenient for evolutionary computation.
Let see an example of how to use the creator to setup an individual that
contains an array of booleans and a maximizing fitness. We will first need to
import the deap.base
and deap.creator
modules.
The creator defines at first a single function create()
that is used to create types. The create()
function takes
at least 2 arguments plus additional optional arguments. The first argument
name is the actual name of the type that we want to create. The second
argument base is the base classe that the new type created should inherit
from. Finally the optional arguments are members to add to the new type, for
example a fitness
for an individual or speed
for a particle.
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
The first line creates a maximizing fitness by replacing, in the base type
Fitness
, the pure virtual
weights
attribute by (1.0,)
that means to
maximize a single objective fitness. The second line creates an
Individual
class that inherits the properties of list
and
has a fitness
attribute of the type FitnessMax
that was just
created.
In this last step, two things are of major importance. The first is the comma
following the 1.0
in the weights declaration, even when implementing a
single objective fitness, the weights (and values) must be iterable. We
won’t repeat it enough, in DEAP single objective is a special case of
multiobjective. The second important thing is how the just created
FitnessMax
can be used directly as if it was part of the
creator
. This is not magic.
Toolbox¶
A Toolbox
can be found in the base module. It is intended
to store functions with their arguments. The toolbox contains two
methods, register()
and
unregister()
that are used to do the tricks.
toolbox = base.Toolbox()
# Attribute generator
toolbox.register("attr_bool", random.randint, 0, 1)
# Structure initializers
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attr_bool, 100)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
In this code block we registered a generation function and two initialization
functions. The generator toolbox.attr_bool()
when called, will draw a
random integer between 0 and 1. The two initializers for their part will
produce respectively initialized individuals and populations.
Again, looking a little closer shows that their is no magic. The registration
of tools in the toolbox only associates an alias to an already existing
function and freezes part of its arguments. This allows to call the alias as
if the majority of the (or every) arguments have already been given. For
example, the attr_bool()
generator is made from the
randint()
that takes two arguments a and b, with a <= n <=
b
, where n is the returned integer. Here, we fix a = 0
and b = 1
.
It is the same thing for the initializers. This time, the
initRepeat()
is frozen with predefined arguments. In the
case of the individual()
method, initRepeat()
takes 3
arguments, a class that is a container – here the Individual
is
derived from a list
–, a function to fill the container and the
number of times the function shall be repeated. When called, the
individual()
method will thus return an individual initialized with what
would be returned by 100 calls to the attr_bool()
method. Finally, the
population()
method uses the same paradigm, but we don’t fix the number
of individuals that it should contain.
The Evaluation Function¶
The evaluation function is pretty simple in this case, we need to count the number of ones in the individual. This is done by the following lines of code.
def evalOneMax(individual):
return sum(individual),
The returned value must be an iterable of length equal to the number of objectives (weights).
The Genetic Operators¶
There is two way of using operators, the first one, is to simply call the
function from the tools
module and the second one is to register
them with their argument in a toolbox as for the initialization methods. The
most convenient way is to register them in the toolbox, because it allows to
easily switch between operators if desired. The toolbox method is also used in
the algorithms, see the One Max Problem: Short Version for an example.
Registering the operators and their default arguments in the toolbox is done as follow.
toolbox.register("evaluate", evalOneMax)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
The evaluation is given the alias evaluate. Having a single argument being the
individual to evaluate we don’t need to fix any, the individual will be given
later in the algorithm. The mutation, for its part, needs an argument to be
fixed (the independent probability of each attribute to be mutated indpb).
In the algorithms the mutate()
function is called with the signature
mutant, = toolbox.mutate(mutant)
. This is the most convenient way because
each mutation takes a different number of arguments, having those arguments
fixed in the toolbox leave open most of the possibilities to change the
mutation (or crossover, or selection, or evaluation) operator later in your
researches.
Evolving the Population¶
Once the representation and the operators are chosen, we have to define an
algorithm. A good habit to take is to define the algorithm inside a function,
generally named main()
.
Creating the Population¶
Before evolving it, we need to instantiate a population. This step is done effortless using the method we registered in the toolbox.
def main():
pop = toolbox.population(n=300)
pop
will be a list
composed of 300 individuals, n is the
parameter left open earlier in the toolbox. The next thing to do is to
evaluate this brand new population.
# Evaluate the entire population
fitnesses = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
We first map()
the evaluation function to every individual, then assign
their respective fitness. Note that the order in fitnesses
and
population
are the same.
The Appeal of Evolution¶
The evolution of the population is the last thing to accomplish. Let say that
we want to evolve for a fixed number of generation NGEN
, the
evolution will then begin with a simple for statement.
# Begin the evolution
for g in range(NGEN):
print("-- Generation %i --" % g)
Is that simple enough? Lets continue with more complicated things, selecting, mating and mutating the population. The crossover and mutation operators provided within DEAP usually take respectively 2 and 1 individual(s) on input and return 2 and 1 modified individual(s), they also modify inplace these individuals.
In a simple GA, the first step is to select the next generation.
# Select the next generation individuals
offspring = toolbox.select(pop, len(pop))
# Clone the selected individuals
offspring = list(map(toolbox.clone, offspring))
This step creates an offspring list that is an exact copy of the selected
individuals. The toolbox.clone()
method ensure that we don’t own a
reference to the individuals but an completely independent instance.
Next, a simple GA would replace the parents by the produced children
directly in the population. This is what is done by the following lines
of code, where a crossover is applied with probability CXPB
and a
mutation with probability MUTPB
. The del
statement simply
invalidate the fitness of the modified individuals.
# 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
The population now needs to be re-evaluated, we then apply the evaluation as seen earlier, but this time only on the individuals with an invalid fitness.
# 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
And finally, last but not least, we replace the old population by the offspring.
pop[:] = offspring
This is the end of the evolution part, it will continue until the predefined number of generation are accomplished.
Although, some statistics may be gathered on the population, the following lines print the min, max, mean and standard deviation of the population.
# Gather all the fitnesses in one list and print the stats
fits = [ind.fitness.values[0] for ind in pop]
length = len(pop)
mean = sum(fits) / length
sum2 = sum(x*x for x in fits)
std = abs(sum2 / length - mean**2)**0.5
print(" Min %s" % min(fits))
print(" Max %s" % max(fits))
print(" Avg %s" % mean)
print(" Std %s" % std)
A Statistics
object has been defined to facilitate how
statistics are gathered. It is not presented here so that we can focus on the
core and not the gravitating helper objects of DEAP.
The complete examples/ga/onemax.