"""
===============================================
:mod:`ec` -- Evolutionary computation framework
===============================================
This module provides the framework for creating evolutionary computations.
.. Copyright 2012 Aaron Garrett
.. Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
.. The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
.. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
.. module:: ec
.. moduleauthor:: Aaron Garrett <garrett@inspiredintelligence.io>
"""
import collections
import copy
import functools
from inspyred.ec import archivers
from inspyred.ec import generators
from inspyred.ec import migrators
from inspyred.ec import observers
from inspyred.ec import replacers
from inspyred.ec import selectors
from inspyred.ec import terminators
from inspyred.ec import variators
import itertools
import logging
import math
import time
[docs]
class Error(Exception):
"""An empty base exception."""
pass
[docs]
class EvolutionExit(Error):
"""An exception that may be raised and caught to end the evolution.
This is an empty exception class that can be raised by the user
at any point in the code and caught outside of the ``evolve``
method.
.. note::
Be aware that ending the evolution in such a way will almost
certainly produce an erroneous population (e.g., not all
individuals will have been reevaluated, etc.). However, this
approach can be viable if solutions have been archived such
that the current population is not of critical importance.
"""
pass
[docs]
class Bounder(object):
"""Defines a basic bounding function for numeric lists.
This callable class acts as a function that bounds a
numeric list between the lower and upper bounds specified.
These bounds can be single values or lists of values. For
instance, if the candidate is composed of five values, each
of which should be bounded between 0 and 1, you can say
``Bounder([0, 0, 0, 0, 0], [1, 1, 1, 1, 1])`` or just
``Bounder(0, 1)``. If either the ``lower_bound`` or
``upper_bound`` argument is ``None``, the Bounder leaves
the candidate unchanged (which is the default behavior).
As an example, if the bounder above were used on the candidate
``[0.2, -0.1, 0.76, 1.3, 0.4]``, the resulting bounded
candidate would be ``[0.2, 0, 0.76, 1, 0.4]``.
A bounding function is necessary to ensure that all
evolutionary operators respect the legal bounds for
candidates. If the user is using only custom operators
(which would be aware of the problem constraints), then
those can obviously be tailored to enforce the bounds
on the candidates themselves. But the built-in operators
make only minimal assumptions about the candidate solutions.
Therefore, they must rely on an external bounding function
that can be user-specified (so as to contain problem-specific
information).
In general, a user-specified bounding function must accept
two arguments: the candidate to be bounded and the keyword
argument dictionary. Typically, the signature of such a
function would be the following::
bounded_candidate = bounding_function(candidate, args)
This function should return the resulting candidate after
bounding has been performed.
Public Attributes:
- *lower_bound* -- the lower bound for a candidate
- *upper_bound* -- the upper bound for a candidate
"""
def __init__(self, lower_bound=None, upper_bound=None):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
if self.lower_bound is not None and self.upper_bound is not None:
if not isinstance(self.lower_bound, collections.abc.Iterable):
self.lower_bound = itertools.repeat(self.lower_bound)
if not isinstance(self.upper_bound, collections.abc.Iterable):
self.upper_bound = itertools.repeat(self.upper_bound)
def __call__(self, candidate, args):
# The default would be to leave the candidate alone
# unless both bounds are specified.
if self.lower_bound is None or self.upper_bound is None:
return candidate
else:
if not isinstance(self.lower_bound, collections.abc.Iterable):
self.lower_bound = [self.lower_bound] * len(candidate)
if not isinstance(self.upper_bound, collections.abc.Iterable):
self.upper_bound = [self.upper_bound] * len(candidate)
bounded_candidate = candidate
for i, (c, lo, hi) in enumerate(zip(candidate, self.lower_bound,
self.upper_bound)):
bounded_candidate[i] = max(min(c, hi), lo)
return bounded_candidate
[docs]
class DiscreteBounder(object):
"""Defines a basic bounding function for numeric lists of discrete values.
This callable class acts as a function that bounds a
numeric list to a set of legitimate values. It does this by
resolving a given candidate value to the nearest legitimate
value that can be attained. In the event that a candidate value
is the same distance to multiple legitimate values, the legitimate
value appearing earliest in the list will be used.
For instance, if ``[1, 4, 8, 16]`` was used as the *values* parameter,
then the candidate ``[6, 10, 13, 3, 4, 0, 1, 12, 2]`` would be
bounded to ``[4, 8, 16, 4, 4, 1, 1, 8, 1]``.
Public Attributes:
- *values* -- the set of attainable values
- *lower_bound* -- the smallest attainable value
- *upper_bound* -- the largest attainable value
"""
def __init__(self, values):
self.values = values
self.lower_bound = itertools.repeat(min(self.values))
self.upper_bound = itertools.repeat(max(self.values))
def __call__(self, candidate, args):
if not isinstance(self.lower_bound, collections.abc.Iterable):
self.lower_bound = [min(self.values)] * len(candidate)
if not isinstance(self.upper_bound, collections.abc.Iterable):
self.upper_bound = [max(self.values)] * len(candidate)
closest = lambda target: min(self.values, key=lambda x: abs(x-target))
bounded_candidate = candidate
for i, c in enumerate(bounded_candidate):
bounded_candidate[i] = closest(c)
return bounded_candidate
[docs]
class Individual(object):
"""Represents an individual in an evolutionary computation.
An individual is defined by its candidate solution and the
fitness (or value) of that candidate solution. Individuals
can be compared with one another by using <, <=, >, and >=.
In all cases, such comparisons are made using the individuals'
fitness values. The ``maximize`` attribute is respected in all
cases, so it is better to think of, for example, < (less-than)
to really mean "worse than" and > (greater-than) to mean
"better than". For instance, if individuals a and b have fitness
values 2 and 4, respectively, and if ``maximize`` were ``True``,
then a < b would be true. If ``maximize`` were ``False``, then
a < b would be false (because a is "better than" b in terms of
the fitness evaluation, since we're minimizing).
.. note::
``Individual`` objects are almost always created by the EC,
rather than the user. The ``evolve`` method of the EC also
has a ``maximize`` argument, whose value is passed directly
to all created individuals.
Public Attributes:
- *candidate* -- the candidate solution
- *fitness* -- the value of the candidate solution
- *birthdate* -- the system time at which the individual was created
- *maximize* -- Boolean value stating use of maximization
"""
def __init__(self, candidate=None, maximize=True):
self._candidate = candidate
self.fitness = None
self.birthdate = time.time()
self.maximize = maximize
@property
def candidate(self):
return self._candidate
@candidate.setter
def candidate(self, value):
self._candidate = value
self.fitness = None
def __str__(self):
return '{0} : {1}'.format(str(self.candidate), str(self.fitness))
def __repr__(self):
return '<Individual: candidate = {0}, fitness = {1}, birthdate = {2}>'.format(str(self.candidate), str(self.fitness), self.birthdate)
def __lt__(self, other):
if self.fitness is not None and other.fitness is not None:
if self.maximize:
return self.fitness < other.fitness
else:
return self.fitness > other.fitness
else:
raise Error('fitness cannot be None when comparing Individuals')
def __le__(self, other):
return self < other or not other < self
def __gt__(self, other):
if self.fitness is not None and other.fitness is not None:
return other < self
else:
raise Error('fitness cannot be None when comparing Individuals')
def __ge__(self, other):
return other < self or not self < other
def __eq__(self, other):
return ((self._candidate, self.fitness, self.maximize) ==
(other._candidate, other.fitness, other.maximize))
def __ne__(self, other):
return not (self == other)
[docs]
class EvolutionaryComputation(object):
"""Represents a basic evolutionary computation.
This class encapsulates the components of a generic evolutionary
computation. These components are the selection mechanism, the
variation operators, the replacement mechanism, the migration
scheme, the archival mechanism, the terminators, and the observers.
The ``observer``, ``terminator``, and ``variator`` attributes may be
specified as lists of such operators. In the case of the ``observer``,
all elements of the list will be called in sequence during the
observation phase. In the case of the ``terminator``, all elements of
the list will be combined via logical ``or`` and, thus, the evolution will
terminate if any of the terminators return True. Finally, in the case
of the ``variator``, the elements of the list will be applied one
after another in pipeline fashion, where the output of one variator
is used as the input to the next.
Public Attributes:
- *selector* -- the selection operator (defaults to ``default_selection``)
- *variator* -- the (possibly list of) variation operator(s) (defaults to
``default_variation``)
- *replacer* -- the replacement operator (defaults to
``default_replacement``)
- *migrator* -- the migration operator (defaults to ``default_migration``)
- *archiver* -- the archival operator (defaults to ``default_archiver``)
- *observer* -- the (possibly list of) observer(s) (defaults to
``default_observer``)
- *terminator* -- the (possibly list of) terminator(s) (defaults to
``default_termination``)
- *logger* -- the logger to use (defaults to the logger 'inspyred.ec')
The following attributes do not have legitimate values until after
the ``evolve`` method executes:
- *termination_cause* -- the name of the function causing
``evolve`` to terminate, in the event that multiple terminators are used
- *generator* -- the generator function passed to ``evolve``
- *evaluator* -- the evaluator function passed to ``evolve``
- *bounder* -- the bounding function passed to ``evolve``
- *maximize* -- Boolean stating use of maximization passed to ``evolve``
- *archive* -- the archive of individuals
- *population* -- the population of individuals
- *num_evaluations* -- the number of fitness evaluations used
- *num_generations* -- the number of generations processed
Note that the attributes above are, in general, not intended to
be modified by the user. (They are intended for the user to query
during or after the ``evolve`` method's execution.) However,
there may be instances where it is necessary to modify them
within other functions. This is possible to do, but it should be the
exception, rather than the rule.
If logging is desired, the following basic code segment can be
used in the ``main`` or calling scope to accomplish that::
import logging
logger = logging.getLogger('inspyred.ec')
logger.setLevel(logging.DEBUG)
file_handler = logging.FileHandler('inspyred.log', mode='w')
file_handler.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
Protected Attributes:
- *_random* -- the random number generator object
- *_kwargs* -- the dictionary of keyword arguments initialized
from the *args* parameter in the *evolve* method
"""
def __init__(self, random):
self.selector = selectors.default_selection
self.variator = variators.default_variation
self.replacer = replacers.default_replacement
self.migrator = migrators.default_migration
self.observer = observers.default_observer
self.archiver = archivers.default_archiver
self.terminator = terminators.default_termination
self.termination_cause = None
self.generator = None
self.evaluator = None
self.bounder = None
self.maximize = True
self.archive = None
self.population = None
self.num_evaluations = 0
self.num_generations = 0
self.logger = logging.getLogger('inspyred.ec')
try:
self.logger.addHandler(logging.NullHandler())
except AttributeError:
# If Python < 2.7, then NullHandler doesn't exist.
pass
self._random = random
self._kwargs = dict()
def _should_terminate(self, pop, ng, ne):
terminate = False
fname = ''
if isinstance(self.terminator, collections.abc.Iterable):
terminators = self.terminator
else:
terminators = [self.terminator]
for clause in terminators:
self.logger.debug('termination test using {0} at generation {1} and evaluation {2}'.format(clause.__name__, ng, ne))
terminate = terminate or clause(population=pop, num_generations=ng, num_evaluations=ne, args=self._kwargs)
if terminate:
fname = clause.__name__
break
if terminate:
self.termination_cause = fname
self.logger.debug('termination from {0} at generation {1} and evaluation {2}'.format(self.termination_cause, ng, ne))
return terminate
[docs]
def evolve(self, generator, evaluator, pop_size=100, seeds=None, maximize=True, bounder=None, **args):
"""Perform the evolution.
This function creates a population and then runs it through a series
of evolutionary epochs until the terminator is satisfied. The general
outline of an epoch is selection, variation, evaluation, replacement,
migration, archival, and observation. The function returns a list of
elements of type ``Individual`` representing the individuals contained
in the final population.
Arguments:
- *generator* -- the function to be used to generate candidate solutions
- *evaluator* -- the function to be used to evaluate candidate solutions
- *pop_size* -- the number of Individuals in the population (default 100)
- *seeds* -- an iterable collection of candidate solutions to include
in the initial population (default None)
- *maximize* -- Boolean value stating use of maximization (default True)
- *bounder* -- a function used to bound candidate solutions (default None)
- *args* -- a dictionary of keyword arguments
The *bounder* parameter, if left as ``None``, will be initialized to a
default ``Bounder`` object that performs no bounding on candidates.
Note that the *_kwargs* class variable will be initialized to the *args*
parameter here. It will also be modified to include the following 'built-in'
keyword argument:
- *_ec* -- the evolutionary computation (this object)
"""
self._kwargs = args
self._kwargs['_ec'] = self
if seeds is None:
seeds = []
if bounder is None:
bounder = Bounder()
self.termination_cause = None
self.generator = generator
self.evaluator = evaluator
self.bounder = bounder
self.maximize = maximize
self.population = []
self.archive = []
# Create the initial population.
if not isinstance(seeds, collections.abc.Sequence):
seeds = [seeds]
initial_cs = copy.copy(seeds)
num_generated = max(pop_size - len(seeds), 0)
i = 0
self.logger.debug('generating initial population')
while i < num_generated:
cs = generator(random=self._random, args=self._kwargs)
initial_cs.append(cs)
i += 1
self.logger.debug('evaluating initial population')
initial_fit = evaluator(candidates=initial_cs, args=self._kwargs)
for cs, fit in zip(initial_cs, initial_fit):
if fit is not None:
ind = Individual(cs, maximize=maximize)
ind.fitness = fit
self.population.append(ind)
else:
self.logger.warning('excluding candidate {0} because fitness received as None'.format(cs))
self.logger.debug('population size is now {0}'.format(len(self.population)))
self.num_evaluations = len(initial_fit)
self.num_generations = 0
self.logger.debug('archiving initial population')
self.archive = self.archiver(random=self._random, population=list(self.population), archive=list(self.archive), args=self._kwargs)
self.logger.debug('archive size is now {0}'.format(len(self.archive)))
self.logger.debug('population size is now {0}'.format(len(self.population)))
# Turn observers and variators into lists if not already
if isinstance(self.observer, collections.abc.Iterable):
observers = self.observer
else:
observers = [self.observer]
if isinstance(self.variator, collections.abc.Iterable):
variators = self.variator
else:
variators = [self.variator]
for obs in observers:
self.logger.debug('observation using {0} at generation {1} and evaluation {2}'.format(obs.__name__, self.num_generations, self.num_evaluations))
obs(population=list(self.population), num_generations=self.num_generations, num_evaluations=self.num_evaluations, args=self._kwargs)
while not self._should_terminate(list(self.population), self.num_generations, self.num_evaluations):
# Select individuals.
self.logger.debug('selection using {0} at generation {1} and evaluation {2}'.format(self.selector.__name__, self.num_generations, self.num_evaluations))
parents = self.selector(random=self._random, population=list(self.population), args=self._kwargs)
self.logger.debug('selected {0} candidates'.format(len(parents)))
offspring_cs = [copy.deepcopy(i.candidate) for i in parents]
for op in variators:
self.logger.debug('variation using {0} at generation {1} and evaluation {2}'.format(op.__name__, self.num_generations, self.num_evaluations))
offspring_cs = op(random=self._random, candidates=offspring_cs, args=self._kwargs)
self.logger.debug('created {0} offspring'.format(len(offspring_cs)))
# Evaluate offspring.
self.logger.debug('evaluation using {0} at generation {1} and evaluation {2}'.format(evaluator.__name__, self.num_generations, self.num_evaluations))
offspring_fit = evaluator(candidates=offspring_cs, args=self._kwargs)
offspring = []
for cs, fit in zip(offspring_cs, offspring_fit):
if fit is not None:
off = Individual(cs, maximize=maximize)
off.fitness = fit
offspring.append(off)
else:
self.logger.warning('excluding candidate {0} because fitness received as None'.format(cs))
self.num_evaluations += len(offspring_fit)
# Replace individuals.
self.logger.debug('replacement using {0} at generation {1} and evaluation {2}'.format(self.replacer.__name__, self.num_generations, self.num_evaluations))
self.population = self.replacer(random=self._random, population=self.population, parents=parents, offspring=offspring, args=self._kwargs)
self.logger.debug('population size is now {0}'.format(len(self.population)))
# Migrate individuals.
self.logger.debug('migration using {0} at generation {1} and evaluation {2}'.format(self.migrator.__name__, self.num_generations, self.num_evaluations))
self.population = self.migrator(random=self._random, population=self.population, args=self._kwargs)
self.logger.debug('population size is now {0}'.format(len(self.population)))
# Archive individuals.
self.logger.debug('archival using {0} at generation {1} and evaluation {2}'.format(self.archiver.__name__, self.num_generations, self.num_evaluations))
self.archive = self.archiver(random=self._random, archive=self.archive, population=list(self.population), args=self._kwargs)
self.logger.debug('archive size is now {0}'.format(len(self.archive)))
self.logger.debug('population size is now {0}'.format(len(self.population)))
self.num_generations += 1
for obs in observers:
self.logger.debug('observation using {0} at generation {1} and evaluation {2}'.format(obs.__name__, self.num_generations, self.num_evaluations))
obs(population=list(self.population), num_generations=self.num_generations, num_evaluations=self.num_evaluations, args=self._kwargs)
return self.population
[docs]
class GA(EvolutionaryComputation):
"""Evolutionary computation representing a canonical genetic algorithm.
This class represents a genetic algorithm which uses, by
default, rank selection, `n`-point crossover, bit-flip mutation,
and generational replacement. In the case of bit-flip mutation,
it is expected that each candidate solution is a ``Sequence``
of binary values.
Optional keyword arguments in ``evolve`` args parameter:
- *num_selected* -- the number of individuals to be selected
(default len(population))
- *crossover_rate* -- the rate at which crossover is performed
(default 1.0)
- *num_crossover_points* -- the `n` crossover points used (default 1)
- *mutation_rate* -- the rate at which mutation is performed (default 0.1)
- *num_elites* -- number of elites to consider (default 0)
"""
def __init__(self, random):
EvolutionaryComputation.__init__(self, random)
self.selector = selectors.rank_selection
self.variator = [variators.n_point_crossover, variators.bit_flip_mutation]
self.replacer = replacers.generational_replacement
[docs]
def evolve(self, generator, evaluator, pop_size=100, seeds=None, maximize=True, bounder=None, **args):
args.setdefault('num_selected', pop_size)
return EvolutionaryComputation.evolve(self, generator, evaluator, pop_size, seeds, maximize, bounder, **args)
[docs]
class ES(EvolutionaryComputation):
"""Evolutionary computation representing a canonical evolution strategy.
This class represents an evolution strategy which uses, by
default, the default selection (i.e., all individuals are selected),
an internal adaptive mutation using strategy parameters, and 'plus'
replacement. It is expected that each candidate solution is a ``Sequence``
of real values.
The candidate solutions to an ES are augmented by strategy parameters of
the same length (using ``inspyred.ec.generators.strategize``). These
strategy parameters are evolved along with the candidates and are used as
the mutation rates for each element of the candidates. The evaluator is
modified internally to use only the actual candidate elements (rather than
also the strategy parameters), so normal evaluator functions may be used
seamlessly.
Optional keyword arguments in ``evolve`` args parameter:
- *tau* -- a proportionality constant (default None)
- *tau_prime* -- a proportionality constant (default None)
- *epsilon* -- the minimum allowed strategy parameter (default 0.00001)
If *tau* is ``None``, it will be set to ``1 / sqrt(2 * sqrt(n))``, where
``n`` is the length of a candidate. If *tau_prime* is ``None``, it will be
set to ``1 / sqrt(2 * n)``. The strategy parameters are updated as follows:
.. math::
\\sigma_i^\\prime = \\sigma_i * e^{\\tau \\cdot N(0, 1) + \\tau^\\prime \\cdot N(0, 1)}
\\sigma_i^\\prime = max(\\sigma_i^\\prime, \\epsilon)
"""
def __init__(self, random):
EvolutionaryComputation.__init__(self, random)
self.selector = selectors.default_selection
self.variator = self._internal_variation
self.replacer = replacers.plus_replacement
def _internal_variation(self, random, candidates, args):
tau = args.setdefault('tau', None)
tau_prime = args.setdefault('tau_prime', None)
epsilon = args.setdefault('epsilon', 0.00001)
mutants = []
n = len(candidates[0]) // 2
if tau is None:
tau = 1 / math.sqrt(2 * math.sqrt(n))
if tau_prime is None:
tau_prime = 1 / math.sqrt(2 * n)
for candidate in candidates:
cand = candidate[:n]
strat = candidate[n:]
for i, s in enumerate(strat):
strat[i] = s * math.exp(tau_prime * random.gauss(0, 1) + tau * random.gauss(0, 1))
strat[i] = max(strat[i], epsilon)
for i, (c, s) in enumerate(zip(cand, strat)):
cand[i] = c + random.gauss(0, s)
cand = self.bounder(cand, args)
cand.extend(strat)
mutants.append(cand)
return mutants
def _internal_evaluator(self, func):
@functools.wraps(func)
def evaluator(candidates, args):
cands = []
for candidate in candidates:
n = len(candidate) // 2
cands.append(candidate[:n])
return func(cands, args)
return evaluator
[docs]
def evolve(self, generator, evaluator, pop_size=100, seeds=None, maximize=True, bounder=None, **args):
generator = generators.strategize(generator)
evaluator = self._internal_evaluator(evaluator)
# Strategize any seeds that are passed.
strategy_seeds = None
if seeds is not None:
strategy_seeds = []
for candidate in seeds:
n = len(candidate)
c = copy.copy(candidate)
c.extend([self._random.random() for _ in range(n)])
strategy_seeds.append(c)
return EvolutionaryComputation.evolve(self, generator, evaluator, pop_size, strategy_seeds, maximize, bounder, **args)
[docs]
class EDA(EvolutionaryComputation):
"""Evolutionary computation representing a canonical estimation of distribution algorithm.
This class represents an estimation of distribution algorithm which
uses, by default, truncation selection, an internal estimation of
distribution variation, and generational replacement. It is expected
that each candidate solution is a ``Sequence`` of real values.
The variation used here creates a statistical model based on the set
of candidates. The offspring are then generated from this model. This
function also makes use of the bounder function as specified in the EC's
``evolve`` method.
Optional keyword arguments in ``evolve`` args parameter:
- *num_selected* -- the number of individuals to be selected
(default len(population)/2)
- *num_offspring* -- the number of offspring to create (default len(population))
- *num_elites* -- number of elites to consider (default 0)
"""
def __init__(self, random):
EvolutionaryComputation.__init__(self, random)
self.selector = selectors.truncation_selection
self.variator = self._internal_variation
self.replacer = replacers.generational_replacement
def _internal_variation(self, random, candidates, args):
num_offspring = args.setdefault('num_offspring', 1)
bounder = args['_ec'].bounder
num_genes = max([len(x) for x in candidates])
genes = [[x[i] for x in candidates] for i in range(num_genes)]
mean = [float(sum(x)) / float(len(x)) for x in genes]
stdev = [sum([(x - m)**2 for x in g]) / float(len(g) - 1) for g, m in zip(genes, mean)]
offspring = []
for _ in range(num_offspring):
child = copy.copy(candidates[0])
for i, (m, s) in enumerate(zip(mean, stdev)):
child[i] = m + random.gauss(0, s)
child = bounder(child, args)
offspring.append(child)
return offspring
[docs]
def evolve(self, generator, evaluator, pop_size=100, seeds=None, maximize=True, bounder=None, **args):
args.setdefault('num_selected', pop_size // 2)
args.setdefault('num_offspring', pop_size)
return EvolutionaryComputation.evolve(self, generator, evaluator, pop_size, seeds, maximize, bounder, **args)
[docs]
class DEA(EvolutionaryComputation):
"""Evolutionary computation representing a differential evolutionary algorithm.
This class represents a differential evolutionary algorithm which uses, by
default, tournament selection, heuristic crossover, Gaussian mutation,
and steady-state replacement. It is expected that each candidate solution
is a ``Sequence`` of real values.
Optional keyword arguments in ``evolve`` args parameter:
- *num_selected* -- the number of individuals to be selected (default 2)
- *tournament_size* -- the tournament size (default 2)
- *crossover_rate* -- the rate at which crossover is performed
(default 1.0)
- *mutation_rate* -- the rate at which mutation is performed (default 0.1)
- *gaussian_mean* -- the mean used in the Gaussian function (default 0)
- *gaussian_stdev* -- the standard deviation used in the Gaussian function
(default 1)
"""
def __init__(self, random):
EvolutionaryComputation.__init__(self, random)
self.selector = selectors.tournament_selection
self.variator = [variators.heuristic_crossover, variators.gaussian_mutation]
self.replacer = replacers.steady_state_replacement
[docs]
def evolve(self, generator, evaluator, pop_size=100, seeds=None, maximize=True, bounder=None, **args):
args.setdefault('num_selected', 2)
return EvolutionaryComputation.evolve(self, generator, evaluator, pop_size, seeds, maximize, bounder, **args)
[docs]
class SA(EvolutionaryComputation):
"""Evolutionary computation representing simulated annealing.
This class represents a simulated annealing algorithm. It accomplishes this
by using default selection (i.e., all individuals are parents), Gaussian
mutation, and simulated annealing replacement. It is expected that each
candidate solution is a ``Sequence`` of real values. Consult the
documentation for the ``simulated_annealing_replacement`` for more
details on the keyword arguments listed below.
.. note::
The ``pop_size`` parameter to ``evolve`` will always be set to 1,
even if a different value is passed.
Optional keyword arguments in ``evolve`` args parameter:
- *temperature* -- the initial temperature
- *cooling_rate* -- a real-valued coefficient in the range (0, 1)
by which the temperature should be reduced
- *mutation_rate* -- the rate at which mutation is performed (default 0.1)
- *gaussian_mean* -- the mean used in the Gaussian function (default 0)
- *gaussian_stdev* -- the standard deviation used in the Gaussian function
(default 1)
"""
def __init__(self, random):
EvolutionaryComputation.__init__(self, random)
self.selector = selectors.default_selection
self.variator = variators.gaussian_mutation
self.replacer = replacers.simulated_annealing_replacement
[docs]
def evolve(self, generator, evaluator, pop_size=1, seeds=None, maximize=True, bounder=None, **args):
pop_size = 1
return EvolutionaryComputation.evolve(self, generator, evaluator, pop_size, seeds, maximize, bounder, **args)