Ga
Genetic algorithm variants: GeneticAlgorithm, Cosyne.
Cosyne (SearchAlgorithm, SinglePopulationAlgorithmMixin)
¶
Implementation of the CoSyNE algorithm.
References:
F.Gomez, J.Schmidhuber, R.Miikkulainen, M.Mitchell (2008).
Accelerated Neural Evolution through Cooperatively Coevolved Synapses.
Journal of Machine Learning Research 9 (5).
Source code in evotorch/algorithms/ga.py
class Cosyne(SearchAlgorithm, SinglePopulationAlgorithmMixin):
"""
Implementation of the CoSyNE algorithm.
References:
F.Gomez, J.Schmidhuber, R.Miikkulainen, M.Mitchell (2008).
Accelerated Neural Evolution through Cooperatively Coevolved Synapses.
Journal of Machine Learning Research 9 (5).
"""
def __init__(
self,
problem: Problem,
*,
popsize: int,
tournament_size: int,
mutation_stdev: Optional[float],
mutation_probability: Optional[float] = None,
permute_all: bool = False,
num_elites: Optional[int] = None,
elitism_ratio: Optional[float] = None,
eta: Optional[float] = None,
num_children: Optional[int] = None,
):
"""
`__init__(...)`: Initialize the Cosyne instance.
Args:
problem: The problem object to work on.
popsize: Population size, as an integer.
tournament_size: Tournament size, for tournament selection.
mutation_stdev: Standard deviation of the Gaussian mutation.
See [GaussianMutation][evotorch.operators.real.GaussianMutation] for more information.
mutation_probability: Elementwise Gaussian mutation probability.
Defaults to None.
See [GaussianMutation][evotorch.operators.real.GaussianMutation] for more information.
permute_all: If given as True, all solutions are subject to
permutation. If given as False (which is the default),
there will be a selection procedure for each decision
variable.
num_elites: Optionally expected as an integer, specifying the
number of elites to pass to the next generation.
Cannot be used together with the argument `elitism_ratio`.
elitism_ratio: Optionally expected as a real number between
0 and 1, specifying the amount of elites to pass to the
next generation. For example, 0.1 means that the best 10%
of the population are accepted as elites and passed onto
the next generation.
Cannot be used together with the argument `num_elites`.
eta: Optionally expected as an integer, specifying the eta
hyperparameter for the simulated binary cross-over (SBX).
If left as None, one-point cross-over will be used instead.
num_children: Number of children to generate at each iteration.
If left as None, then this number is half of the population
size.
"""
problem.ensure_numeric()
SearchAlgorithm.__init__(self, problem)
if mutation_stdev is None:
if mutation_probability is not None:
raise ValueError(
f"`mutation_probability` was set to {mutation_probability}, but `mutation_stdev` is None, "
"which means, mutation is disabled. If you want to enable the mutation, be sure to provide "
"`mutation_stdev` as well."
)
self.mutation_op = None
else:
self.mutation_op = GaussianMutation(
self._problem,
stdev=mutation_stdev,
mutation_probability=mutation_probability,
)
cross_over_kwargs = {"tournament_size": tournament_size}
if num_children is None:
cross_over_kwargs["cross_over_rate"] = 2.0
else:
cross_over_kwargs["num_children"] = num_children
if eta is None:
self._cross_over_op = OnePointCrossOver(self._problem, **cross_over_kwargs)
else:
self._cross_over_op = SimulatedBinaryCrossOver(self._problem, eta=eta, **cross_over_kwargs)
self._permutation_op = CosynePermutation(self._problem, permute_all=permute_all)
self._popsize = int(popsize)
if num_elites is not None and elitism_ratio is None:
self._num_elites = int(num_elites)
elif num_elites is None and elitism_ratio is not None:
self._num_elites = int(self._popsize * elitism_ratio)
elif num_elites is None and elitism_ratio is None:
self._num_elites = None
else:
raise ValueError(
"Received both `num_elites` and `elitism_ratio`. Please provide only one of them, or none of them."
)
self._population = SolutionBatch(problem, device=problem.device, popsize=self._popsize)
self._first_generation: bool = True
# GAStatusMixin.__init__(self)
SinglePopulationAlgorithmMixin.__init__(self)
@property
def population(self) -> SolutionBatch:
return self._population
def _step(self):
if self._first_generation:
self._first_generation = False
self._problem.evaluate(self._population)
to_merge = []
num_elites = self._num_elites
num_parents = int(self._popsize / 4)
num_relevant = max((0 if num_elites is None else num_elites), num_parents)
sorted_relevant = self._population.take_best(num_relevant)
if self._num_elites is not None and self._num_elites >= 1:
to_merge.append(sorted_relevant[:num_elites].clone())
parents = sorted_relevant[:num_parents]
children = self._cross_over_op(parents)
if self.mutation_op is not None:
children = self.mutation_op(children)
permuted = self._permutation_op(self._population)
to_merge.extend([children, permuted])
extended_population = SolutionBatch(merging_of=to_merge)
self._problem.evaluate(extended_population)
self._population = extended_population.take_best(self._popsize)
__init__(self, problem, *, popsize, tournament_size, mutation_stdev, mutation_probability=None, permute_all=False, num_elites=None, elitism_ratio=None, eta=None, num_children=None)
special
¶
__init__(...): Initialize the Cosyne instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
problem |
Problem |
The problem object to work on. |
required |
popsize |
int |
Population size, as an integer. |
required |
tournament_size |
int |
Tournament size, for tournament selection. |
required |
mutation_stdev |
Optional[float] |
Standard deviation of the Gaussian mutation. See GaussianMutation for more information. |
required |
mutation_probability |
Optional[float] |
Elementwise Gaussian mutation probability. Defaults to None. See GaussianMutation for more information. |
None |
permute_all |
bool |
If given as True, all solutions are subject to permutation. If given as False (which is the default), there will be a selection procedure for each decision variable. |
False |
num_elites |
Optional[int] |
Optionally expected as an integer, specifying the
number of elites to pass to the next generation.
Cannot be used together with the argument |
None |
elitism_ratio |
Optional[float] |
Optionally expected as a real number between
0 and 1, specifying the amount of elites to pass to the
next generation. For example, 0.1 means that the best 10%
of the population are accepted as elites and passed onto
the next generation.
Cannot be used together with the argument |
None |
eta |
Optional[float] |
Optionally expected as an integer, specifying the eta hyperparameter for the simulated binary cross-over (SBX). If left as None, one-point cross-over will be used instead. |
None |
num_children |
Optional[int] |
Number of children to generate at each iteration. If left as None, then this number is half of the population size. |
None |
Source code in evotorch/algorithms/ga.py
def __init__(
self,
problem: Problem,
*,
popsize: int,
tournament_size: int,
mutation_stdev: Optional[float],
mutation_probability: Optional[float] = None,
permute_all: bool = False,
num_elites: Optional[int] = None,
elitism_ratio: Optional[float] = None,
eta: Optional[float] = None,
num_children: Optional[int] = None,
):
"""
`__init__(...)`: Initialize the Cosyne instance.
Args:
problem: The problem object to work on.
popsize: Population size, as an integer.
tournament_size: Tournament size, for tournament selection.
mutation_stdev: Standard deviation of the Gaussian mutation.
See [GaussianMutation][evotorch.operators.real.GaussianMutation] for more information.
mutation_probability: Elementwise Gaussian mutation probability.
Defaults to None.
See [GaussianMutation][evotorch.operators.real.GaussianMutation] for more information.
permute_all: If given as True, all solutions are subject to
permutation. If given as False (which is the default),
there will be a selection procedure for each decision
variable.
num_elites: Optionally expected as an integer, specifying the
number of elites to pass to the next generation.
Cannot be used together with the argument `elitism_ratio`.
elitism_ratio: Optionally expected as a real number between
0 and 1, specifying the amount of elites to pass to the
next generation. For example, 0.1 means that the best 10%
of the population are accepted as elites and passed onto
the next generation.
Cannot be used together with the argument `num_elites`.
eta: Optionally expected as an integer, specifying the eta
hyperparameter for the simulated binary cross-over (SBX).
If left as None, one-point cross-over will be used instead.
num_children: Number of children to generate at each iteration.
If left as None, then this number is half of the population
size.
"""
problem.ensure_numeric()
SearchAlgorithm.__init__(self, problem)
if mutation_stdev is None:
if mutation_probability is not None:
raise ValueError(
f"`mutation_probability` was set to {mutation_probability}, but `mutation_stdev` is None, "
"which means, mutation is disabled. If you want to enable the mutation, be sure to provide "
"`mutation_stdev` as well."
)
self.mutation_op = None
else:
self.mutation_op = GaussianMutation(
self._problem,
stdev=mutation_stdev,
mutation_probability=mutation_probability,
)
cross_over_kwargs = {"tournament_size": tournament_size}
if num_children is None:
cross_over_kwargs["cross_over_rate"] = 2.0
else:
cross_over_kwargs["num_children"] = num_children
if eta is None:
self._cross_over_op = OnePointCrossOver(self._problem, **cross_over_kwargs)
else:
self._cross_over_op = SimulatedBinaryCrossOver(self._problem, eta=eta, **cross_over_kwargs)
self._permutation_op = CosynePermutation(self._problem, permute_all=permute_all)
self._popsize = int(popsize)
if num_elites is not None and elitism_ratio is None:
self._num_elites = int(num_elites)
elif num_elites is None and elitism_ratio is not None:
self._num_elites = int(self._popsize * elitism_ratio)
elif num_elites is None and elitism_ratio is None:
self._num_elites = None
else:
raise ValueError(
"Received both `num_elites` and `elitism_ratio`. Please provide only one of them, or none of them."
)
self._population = SolutionBatch(problem, device=problem.device, popsize=self._popsize)
self._first_generation: bool = True
# GAStatusMixin.__init__(self)
SinglePopulationAlgorithmMixin.__init__(self)
ExtendedPopulationMixin
¶
A mixin class that provides the method _make_extended_population(...).
This mixin class assumes that the inheriting class has the properties
problem (of type Problem), which provide
and population (of type SolutionBatch),
which provide the associated problem object and the current population,
respectively.
The class which inherits this mixin class gains the method
_make_extended_population(...). This new method applies the operators
specified during the initialization phase of this mixin class on the
current population, produces children, and then returns an extended
population.
Source code in evotorch/algorithms/ga.py
class ExtendedPopulationMixin:
"""
A mixin class that provides the method `_make_extended_population(...)`.
This mixin class assumes that the inheriting class has the properties
`problem` (of type [Problem][evotorch.core.Problem]), which provide
and `population` (of type [SolutionBatch][evotorch.core.SolutionBatch]),
which provide the associated problem object and the current population,
respectively.
The class which inherits this mixin class gains the method
`_make_extended_population(...)`. This new method applies the operators
specified during the initialization phase of this mixin class on the
current population, produces children, and then returns an extended
population.
"""
def __init__(
self,
*,
re_evaluate: bool,
re_evaluate_parents_first: Optional[bool] = None,
operators: Optional[Iterable] = None,
allow_empty_operators_list: bool = False,
):
"""
`__init__(...)`: Initialize the ExtendedPopulationMixin.
Args:
re_evaluate: Whether or not to re-evaluate the parent population
at every generation. When dealing with problems where the
fitness and/or feature evaluations are stochastic, one might
want to set this as True. On the other hand, for when the
fitness and/or feature evaluations are deterministic, one
might prefer to set this as False for efficiency.
re_evaluate_parents_first: This is to be specified only when
`re_evaluate` is True (otherwise to be left as None).
If this is given as True, then it will be assumed that the
provided operators require the parents to be evaluated.
If this is given as False, then it will be assumed that the
provided operators work without looking at the parents'
fitnesses (in which case both parents and children can be
evaluated in a single vectorized computation cycle).
If this is left as None, then whether or not the operators
need to know the parent evaluations will be determined
automatically as follows:
if the operators contain at least one cross-over operator
then `re_evaluate_parents_first` will be internally set as
True; otherwise `re_evaluate_parents_first` will be internally
set as False.
operators: List of operators to apply on the current population
for generating a new extended population.
allow_empty_operators_list: Whether or not to allow the operator
list to be empty. The default and the recommended value
is False. For cases where the inheriting class wants to
decide the operators later (via the attribute `_operators`)
this can be set as True.
"""
self._operators = [] if operators is None else list(operators)
if (not allow_empty_operators_list) and (len(self._operators) == 0):
raise ValueError("Received `operators` as an empty sequence. Please provide at least one operator.")
self._using_cross_over: bool = False
for operator in self._operators:
if isinstance(operator, CrossOver):
self._using_cross_over = True
break
self._re_evaluate: bool = bool(re_evaluate)
if re_evaluate_parents_first is None:
self._re_evaluate_parents_first = self._using_cross_over
else:
if not self._re_evaluate:
raise ValueError(
"Encountered the argument `re_evaluate_parents_first` as something other than None."
" However, `re_evaluate` is given as False."
" Please use `re_evaluate_parents_first` only when `re_evaluate` is True."
)
self._re_evaluate_parents_first = bool(re_evaluate_parents_first)
self._first_iter: bool = True
def _make_extended_population(self, split: bool = False) -> Union[SolutionBatch, tuple]:
"""
Make and return a new extended population that is evaluated.
Args:
split: If False, then the extended population will be returned
as a single SolutionBatch object which contains both the
parents and the children.
If True, then the extended population will be returned
as a pair of SolutionBatch objects, the first one being
the parents and the second one being the children.
Returns:
The extended population.
"""
# Get the problem object and the population
problem: Problem = self.problem
population: SolutionBatch = self.population
if self._re_evaluate:
# This is the case where our mixin is configured to re-evaluate the parents at every generation.
# Set the first iteration indicator to False
self._first_iter = False
if self._re_evaluate_parents_first:
# This is the case where our mixin is configured to evaluate the parents separately first.
# This is a sub-case of `_re_evaluate=True`.
# Evaluate the population, which stores the parents
problem.evaluate(population)
# Now that our parents are evaluated, we use the operators on them and get the children.
children = _use_operators(population, self._operators)
# Evaluate the children
problem.evaluate(children)
if split:
# If our mixin is configured to return the population and the children, then we return a tuple
# containing them as separate items.
return population, children
else:
# If our mixin is configured to return the population and the children in a single batch,
# then we concatenate the population and the children and return the resulting combined batch.
return SolutionBatch.cat([population, children])
else:
# This is the case where our mixin is configured to evaluate the parents and the children in one go.
# This is a sub-case of `_re_evaluate=True`.
# Use the operators on the parent solutions. It does not matter whether or not the parents are evaluated.
children = _use_operators(population, self._operators)
# Form an extended population by concatenating the population and the children.
extended_population = SolutionBatch.cat([population, children])
# Evaluate the extended population in one go.
problem.evaluate(extended_population)
if split:
# The method was configured to return the parents and the children separately.
# Because we combined them earlier for evaluating them in one go, we will split them now.
# Get the number of parents
num_parents = len(population)
# Get the newly evaluated copies of the parents from the extended population
parents = extended_population[:num_parents]
# Get the children from the extended population
children = extended_population[num_parents:]
# Return the newly evaluated copies of the parents and the children separately.
return parents, children
else:
# The method was configured to return the parents and the children in a single SolutionBatch.
# Here, we just return the extended population that we already have produced.
return extended_population
else:
# This is the case where our mixin is configured NOT to re-evaluate the parents at every generation.
if self._first_iter:
# The first iteration indicator (`_first_iter`) is True. So, this is the first iteration.
# We set `_first_iter` to False for future generations.
self._first_iter = False
# We not evaluate the parent population (because the parents are expected to be non-evaluated at the
# beginning).
problem.evaluate(population)
# Here, we assume that the parents are already evaluated. We apply our operators on the parents.
children = _use_operators(population, self._operators)
# Now, we evaluate the children.
problem.evaluate(children)
if split:
# Return the population and the children separately if `split=True`.
return population, children
else:
# Return the population and the children in a single SolutionBatch if `split=False`.
return SolutionBatch.cat([population, children])
@property
def re_evaluate(self) -> bool:
"""
Whether or not this search algorithm re-evaluates the parents
"""
return self._re_evaluate
@property
def re_evaluate_parents_first(self) -> Optional[bool]:
"""
Whether or not this search algorithm re-evaluates the parents separately.
This property is relevant only when `re_evaluate` is True.
If `re_evaluate` is False, then this property will return None.
"""
if self._re_evaluate:
return self._re_evaluate_parents_first
else:
return None
re_evaluate: bool
property
readonly
¶
Whether or not this search algorithm re-evaluates the parents
re_evaluate_parents_first: Optional[bool]
property
readonly
¶
Whether or not this search algorithm re-evaluates the parents separately.
This property is relevant only when re_evaluate is True.
If re_evaluate is False, then this property will return None.
__init__(self, *, re_evaluate, re_evaluate_parents_first=None, operators=None, allow_empty_operators_list=False)
special
¶
__init__(...): Initialize the ExtendedPopulationMixin.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
re_evaluate |
bool |
Whether or not to re-evaluate the parent population at every generation. When dealing with problems where the fitness and/or feature evaluations are stochastic, one might want to set this as True. On the other hand, for when the fitness and/or feature evaluations are deterministic, one might prefer to set this as False for efficiency. |
required |
re_evaluate_parents_first |
Optional[bool] |
This is to be specified only when
|
None |
operators |
Optional[Iterable] |
List of operators to apply on the current population for generating a new extended population. |
None |
allow_empty_operators_list |
bool |
Whether or not to allow the operator
list to be empty. The default and the recommended value
is False. For cases where the inheriting class wants to
decide the operators later (via the attribute |
False |
Source code in evotorch/algorithms/ga.py
def __init__(
self,
*,
re_evaluate: bool,
re_evaluate_parents_first: Optional[bool] = None,
operators: Optional[Iterable] = None,
allow_empty_operators_list: bool = False,
):
"""
`__init__(...)`: Initialize the ExtendedPopulationMixin.
Args:
re_evaluate: Whether or not to re-evaluate the parent population
at every generation. When dealing with problems where the
fitness and/or feature evaluations are stochastic, one might
want to set this as True. On the other hand, for when the
fitness and/or feature evaluations are deterministic, one
might prefer to set this as False for efficiency.
re_evaluate_parents_first: This is to be specified only when
`re_evaluate` is True (otherwise to be left as None).
If this is given as True, then it will be assumed that the
provided operators require the parents to be evaluated.
If this is given as False, then it will be assumed that the
provided operators work without looking at the parents'
fitnesses (in which case both parents and children can be
evaluated in a single vectorized computation cycle).
If this is left as None, then whether or not the operators
need to know the parent evaluations will be determined
automatically as follows:
if the operators contain at least one cross-over operator
then `re_evaluate_parents_first` will be internally set as
True; otherwise `re_evaluate_parents_first` will be internally
set as False.
operators: List of operators to apply on the current population
for generating a new extended population.
allow_empty_operators_list: Whether or not to allow the operator
list to be empty. The default and the recommended value
is False. For cases where the inheriting class wants to
decide the operators later (via the attribute `_operators`)
this can be set as True.
"""
self._operators = [] if operators is None else list(operators)
if (not allow_empty_operators_list) and (len(self._operators) == 0):
raise ValueError("Received `operators` as an empty sequence. Please provide at least one operator.")
self._using_cross_over: bool = False
for operator in self._operators:
if isinstance(operator, CrossOver):
self._using_cross_over = True
break
self._re_evaluate: bool = bool(re_evaluate)
if re_evaluate_parents_first is None:
self._re_evaluate_parents_first = self._using_cross_over
else:
if not self._re_evaluate:
raise ValueError(
"Encountered the argument `re_evaluate_parents_first` as something other than None."
" However, `re_evaluate` is given as False."
" Please use `re_evaluate_parents_first` only when `re_evaluate` is True."
)
self._re_evaluate_parents_first = bool(re_evaluate_parents_first)
self._first_iter: bool = True
GeneticAlgorithm (SearchAlgorithm, SinglePopulationAlgorithmMixin, ExtendedPopulationMixin)
¶
A genetic algorithm implementation.
Basic usage. Let us consider a single-objective optimization problem where the goal is to minimize the L2 norm of a continuous tensor of length 10:
from evotorch import Problem
from evotorch.algorithms import GeneticAlgorithm
from evotorch.operators import OnePointCrossOver, GaussianMutation
import torch
def f(x: torch.Tensor) -> torch.Tensor:
return torch.linalg.norm(x)
problem = Problem(
"min",
f,
initial_bounds=(-10.0, 10.0),
solution_length=10,
)
For solving this problem, a genetic algorithm could be instantiated as follows:
ga = GeneticAlgorithm(
problem,
popsize=100,
operators=[
OnePointCrossOver(problem, tournament_size=4),
GaussianMutation(problem, stdev=0.1),
],
)
The genetic algorithm instantiated above is configured to have a population size of 100, and is configured to perform the following operations on the population at each generation: (i) select solutions with a tournament of size 4, and produce children from the selected solutions by applying one-point cross-over; (ii) apply a gaussian mutation on the values of the solutions produced by the previous step, the amount of the mutation being sampled according to a standard deviation of 0.1. Once instantiated, this GeneticAlgorithm instance can be used with an API compatible with other search algorithms, as shown below:
from evotorch.logging import StdOutLogger
_ = StdOutLogger(ga) # Report the evolution's progress to standard output
ga.run(100) # Run the algorithm for 100 generations
print("Solution with best fitness ever:", ga.status["best"])
print("Current population's best:", ga.status["pop_best"])
Please also note:
- The operators are always executed according to the order specified within
the
operatorsargument. - There are more operators available in the namespace evotorch.operators.
- By default, GeneticAlgorithm is elitist. In the elitist mode, an extended
population is formed from parent solutions and child solutions, and the
best n solutions of this extended population are accepted as the next
generation. If you wish to switch to a non-elitist mode (where children
unconditionally replace the worst-performing parents), you can use the
initialization argument
elitist=False. - It is not mandatory to specify a cross-over operator. When a cross-over operator is missing, the GeneticAlgorithm will work like a simple evolution strategy implementation which produces children by mutating the parents, and then replaces the parents (where the criteria for replacing the parents depend on whether or not elitism is enabled).
- To be able to deal with stochastic fitness functions correctly,
GeneticAlgorithm re-evaluates previously evaluated parents as well.
When you are sure that the fitness function is deterministic,
you can pass the initialization argument
re_evaluate=Falseto prevent unnecessary computations.
Integer decision variables.
GeneticAlgorithm can be used on problems with dtype declared as integer
(e.g. torch.int32, torch.int64, etc.).
Within the field of discrete optimization, it is common to encounter
one or more of these scenarios:
- The search space of the problem has a special structure that one will wish to exploit (within the cross-over and/or mutation operators) to be able to reach the (near-)optimum within a reasonable amount of time.
- The problem is partially or fully combinatorial.
- The problem is constrained in such a way that arbitrarily sampling discrete values for its decision variables might cause infeasibility.
Considering all these scenarios, it is difficult to come up with general cross-over and mutation operators that will work across various discrete optimization problems, and it is common to design problem-specific operators. In EvoTorch, it is possible to define custom operators and use them with GeneticAlgorithm, which is required when using GeneticAlgorithm on a problem with a non-float dtype.
As an example, let us consider the following discrete optimization problem:
def f(x: torch.Tensor) -> torch.Tensor:
return torch.sum(x)
problem = Problem(
"min",
f,
bounds=(-10, 10),
solution_length=10,
dtype=torch.int64,
)
Although EvoTorch does provide a very simple and generic (usable with float and int dtypes) cross-over named OnePointCrossOver (a cross-over which randomly decides a cutting point for each pair of parents, cuts them from those points and recombines them), it can be desirable and necessary to implement a custom cross-over operator. One can inherit from CrossOver to define a custom cross-over operator, as shown below:
from evotorch import SolutionBatch
from evotorch.operators import CrossOver
class CustomCrossOver(CrossOver):
def _do_cross_over(
self,
parents1: torch.Tensor,
parents2: torch.Tensor,
) -> SolutionBatch:
# parents1 is a tensor storing the decision values of the first
# half of the chosen parents.
# parents2 is a tensor storing the decision values of the second
# half of the chosen parents.
# We expect that the lengths of parents1 and parents2 are equal.
assert len(parents1) == len(parents2)
# Allocate an empty SolutionBatch that will store the children
childpop = SolutionBatch(self.problem, popsize=num_parents, empty=True)
# Gain access to the decision values tensor of the newly allocated
# childpop
childpop_values = childpop.access_values()
# Here we somehow fill `childpop_values` by recombining the parents.
# The most common thing to do is to produce two children by
# combining parents1[0] and parents2[0], to produce the next two
# children parents1[1] and parents2[1], and so on.
childpop_values[:] = ...
# Return the child population
return childpop
One can define a custom mutation operator by inheriting from Operator, as shown below:
class CustomMutation(Operator):
def _do(self, solutions: SolutionBatch):
# Get the decision values tensor of the solutions
sln_values = solutions.access_values()
# do in-place modifications to the decision values
sln_values[:] = ...
Alternatively, you could define the mutation operator as a function:
def my_mutation_function(original_values: torch.Tensor) -> torch.Tensor:
# Somehow produce mutated copies of the original values
mutated_values = ...
# Return the mutated values
return mutated_values
With these defined operators, we are now ready to instantiate our GeneticAlgorithm:
ga = GeneticAlgorithm(
problem,
popsize=100,
operators=[
CustomCrossOver(problem, tournament_size=4),
CustomMutation(problem),
# -- or, if you chose to define the mutation as a function: --
# my_mutation_function,
],
)
Non-numeric or variable-length solutions.
GeneticAlgorithm can also work on problems whose dtype is declared
as object, where dtype=object means that a solution's value(s) can be
expressed via a tensor, a numpy array, a scalar, a tuple, a list, a
dictionary.
Like in the previously discussed case (where dtype is an integer type),
one has to define custom operators when working on problems with
dtype=object. A custom cross-over definition specialized for
dtype=object looks like this:
from evotorch.tools import ObjectArray
class CrossOverForObjectDType(CrossOver):
def _do_cross_over(
self,
parents1: ObjectArray,
parents2: ObjectArray,
) -> SolutionBatch:
# Allocate an empty SolutionBatch that will store the children
childpop = SolutionBatch(self.problem, popsize=num_parents, empty=True)
# Gain access to the decision values ObjectArray of the newly allocated
# childpop
childpop_values = childpop.access_values()
# Here we somehow fill `childpop_values` by recombining the parents.
# The most common thing to do is to produce two children by
# combining parents1[0] and parents2[0], to produce the next two
# children parents1[1] and parents2[1], and so on.
childpop_values[:] = ...
# Return the child population
return childpop
A custom mutation operator specialized for dtype=object looks like this:
class MutationForObjectDType(Operator):
def _do(self, solutions: SolutionBatch):
# Get the decision values ObjectArray of the solutions
sln_values = solutions.access_values()
# do in-place modifications to the decision values
sln_values[:] = ...
A custom mutation function specialized for dtype=object looks like this:
def mutation_for_object_dtype(original_values: ObjectArray) -> ObjectArray:
# Somehow produce mutated copies of the original values
mutated_values = ...
# Return the mutated values
return mutated_values
With these operators defined, one can instantiate the GeneticAlgorithm:
ga = GeneticAlgorithm(
problem_with_object_dtype,
popsize=100,
operators=[
CrossOverForObjectDType(problem_with_object_dtype, tournament_size=4),
MutationForObjectDType(problem_with_object_dtype),
# -- or, if you chose to define the mutation as a function: --
# mutation_for_object_dtype,
],
)
Multiple objectives. GeneticAlgorithm can work on problems with multiple objectives. When there are multiple objectives, GeneticAlgorithm will compare the solutions according to their pareto-ranks and their crowding distances, like done by the NSGA-II algorithm (Deb, 2002).
References:
Sean Luke, 2013, Essentials of Metaheuristics, Lulu, second edition
available for free at http://cs.gmu.edu/~sean/book/metaheuristics/
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan (2002).
A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.
Source code in evotorch/algorithms/ga.py
class GeneticAlgorithm(SearchAlgorithm, SinglePopulationAlgorithmMixin, ExtendedPopulationMixin):
"""
A genetic algorithm implementation.
**Basic usage.**
Let us consider a single-objective optimization problem where the goal is to
minimize the L2 norm of a continuous tensor of length 10:
```python
from evotorch import Problem
from evotorch.algorithms import GeneticAlgorithm
from evotorch.operators import OnePointCrossOver, GaussianMutation
import torch
def f(x: torch.Tensor) -> torch.Tensor:
return torch.linalg.norm(x)
problem = Problem(
"min",
f,
initial_bounds=(-10.0, 10.0),
solution_length=10,
)
```
For solving this problem, a genetic algorithm could be instantiated as
follows:
```python
ga = GeneticAlgorithm(
problem,
popsize=100,
operators=[
OnePointCrossOver(problem, tournament_size=4),
GaussianMutation(problem, stdev=0.1),
],
)
```
The genetic algorithm instantiated above is configured to have a population
size of 100, and is configured to perform the following operations on the
population at each generation:
(i) select solutions with a tournament of size 4, and produce children from
the selected solutions by applying one-point cross-over;
(ii) apply a gaussian mutation on the values of the solutions produced by
the previous step, the amount of the mutation being sampled according to a
standard deviation of 0.1.
Once instantiated, this GeneticAlgorithm instance can be used with an API
compatible with other search algorithms, as shown below:
```python
from evotorch.logging import StdOutLogger
_ = StdOutLogger(ga) # Report the evolution's progress to standard output
ga.run(100) # Run the algorithm for 100 generations
print("Solution with best fitness ever:", ga.status["best"])
print("Current population's best:", ga.status["pop_best"])
```
Please also note:
- The operators are always executed according to the order specified within
the `operators` argument.
- There are more operators available in the namespace
[evotorch.operators][evotorch.operators].
- By default, GeneticAlgorithm is elitist. In the elitist mode, an extended
population is formed from parent solutions and child solutions, and the
best n solutions of this extended population are accepted as the next
generation. If you wish to switch to a non-elitist mode (where children
unconditionally replace the worst-performing parents), you can use the
initialization argument `elitist=False`.
- It is not mandatory to specify a cross-over operator. When a cross-over
operator is missing, the GeneticAlgorithm will work like a simple
evolution strategy implementation which produces children by mutating
the parents, and then replaces the parents (where the criteria for
replacing the parents depend on whether or not elitism is enabled).
- To be able to deal with stochastic fitness functions correctly,
GeneticAlgorithm re-evaluates previously evaluated parents as well.
When you are sure that the fitness function is deterministic,
you can pass the initialization argument `re_evaluate=False` to prevent
unnecessary computations.
**Integer decision variables.**
GeneticAlgorithm can be used on problems with `dtype` declared as integer
(e.g. `torch.int32`, `torch.int64`, etc.).
Within the field of discrete optimization, it is common to encounter
one or more of these scenarios:
- The search space of the problem has a special structure that one will
wish to exploit (within the cross-over and/or mutation operators) to
be able to reach the (near-)optimum within a reasonable amount of time.
- The problem is partially or fully combinatorial.
- The problem is constrained in such a way that arbitrarily sampling
discrete values for its decision variables might cause infeasibility.
Considering all these scenarios, it is difficult to come up with general
cross-over and mutation operators that will work across various discrete
optimization problems, and it is common to design problem-specific
operators. In EvoTorch, it is possible to define custom operators and
use them with GeneticAlgorithm, which is required when using
GeneticAlgorithm on a problem with a non-float dtype.
As an example, let us consider the following discrete optimization
problem:
```python
def f(x: torch.Tensor) -> torch.Tensor:
return torch.sum(x)
problem = Problem(
"min",
f,
bounds=(-10, 10),
solution_length=10,
dtype=torch.int64,
)
```
Although EvoTorch does provide a very simple and generic (usable with float
and int dtypes) cross-over named
[OnePointCrossOver][evotorch.operators.real.OnePointCrossOver]
(a cross-over which randomly decides a cutting point for each pair of
parents, cuts them from those points and recombines them), it can be
desirable and necessary to implement a custom cross-over operator.
One can inherit from [CrossOver][evotorch.operators.base.CrossOver] to
define a custom cross-over operator, as shown below:
```python
from evotorch import SolutionBatch
from evotorch.operators import CrossOver
class CustomCrossOver(CrossOver):
def _do_cross_over(
self,
parents1: torch.Tensor,
parents2: torch.Tensor,
) -> SolutionBatch:
# parents1 is a tensor storing the decision values of the first
# half of the chosen parents.
# parents2 is a tensor storing the decision values of the second
# half of the chosen parents.
# We expect that the lengths of parents1 and parents2 are equal.
assert len(parents1) == len(parents2)
# Allocate an empty SolutionBatch that will store the children
childpop = SolutionBatch(self.problem, popsize=num_parents, empty=True)
# Gain access to the decision values tensor of the newly allocated
# childpop
childpop_values = childpop.access_values()
# Here we somehow fill `childpop_values` by recombining the parents.
# The most common thing to do is to produce two children by
# combining parents1[0] and parents2[0], to produce the next two
# children parents1[1] and parents2[1], and so on.
childpop_values[:] = ...
# Return the child population
return childpop
```
One can define a custom mutation operator by inheriting from
[Operator][evotorch.operators.base.Operator], as shown below:
```python
class CustomMutation(Operator):
def _do(self, solutions: SolutionBatch):
# Get the decision values tensor of the solutions
sln_values = solutions.access_values()
# do in-place modifications to the decision values
sln_values[:] = ...
```
Alternatively, you could define the mutation operator as a function:
```python
def my_mutation_function(original_values: torch.Tensor) -> torch.Tensor:
# Somehow produce mutated copies of the original values
mutated_values = ...
# Return the mutated values
return mutated_values
```
With these defined operators, we are now ready to instantiate our
GeneticAlgorithm:
```python
ga = GeneticAlgorithm(
problem,
popsize=100,
operators=[
CustomCrossOver(problem, tournament_size=4),
CustomMutation(problem),
# -- or, if you chose to define the mutation as a function: --
# my_mutation_function,
],
)
```
**Non-numeric or variable-length solutions.**
GeneticAlgorithm can also work on problems whose `dtype` is declared
as `object`, where `dtype=object` means that a solution's value(s) can be
expressed via a tensor, a numpy array, a scalar, a tuple, a list, a
dictionary.
Like in the previously discussed case (where dtype is an integer type),
one has to define custom operators when working on problems with
`dtype=object`. A custom cross-over definition specialized for
`dtype=object` looks like this:
```python
from evotorch.tools import ObjectArray
class CrossOverForObjectDType(CrossOver):
def _do_cross_over(
self,
parents1: ObjectArray,
parents2: ObjectArray,
) -> SolutionBatch:
# Allocate an empty SolutionBatch that will store the children
childpop = SolutionBatch(self.problem, popsize=num_parents, empty=True)
# Gain access to the decision values ObjectArray of the newly allocated
# childpop
childpop_values = childpop.access_values()
# Here we somehow fill `childpop_values` by recombining the parents.
# The most common thing to do is to produce two children by
# combining parents1[0] and parents2[0], to produce the next two
# children parents1[1] and parents2[1], and so on.
childpop_values[:] = ...
# Return the child population
return childpop
```
A custom mutation operator specialized for `dtype=object` looks like this:
```python
class MutationForObjectDType(Operator):
def _do(self, solutions: SolutionBatch):
# Get the decision values ObjectArray of the solutions
sln_values = solutions.access_values()
# do in-place modifications to the decision values
sln_values[:] = ...
```
A custom mutation function specialized for `dtype=object` looks like this:
```python
def mutation_for_object_dtype(original_values: ObjectArray) -> ObjectArray:
# Somehow produce mutated copies of the original values
mutated_values = ...
# Return the mutated values
return mutated_values
```
With these operators defined, one can instantiate the GeneticAlgorithm:
```python
ga = GeneticAlgorithm(
problem_with_object_dtype,
popsize=100,
operators=[
CrossOverForObjectDType(problem_with_object_dtype, tournament_size=4),
MutationForObjectDType(problem_with_object_dtype),
# -- or, if you chose to define the mutation as a function: --
# mutation_for_object_dtype,
],
)
```
**Multiple objectives.**
GeneticAlgorithm can work on problems with multiple objectives.
When there are multiple objectives, GeneticAlgorithm will compare the
solutions according to their pareto-ranks and their crowding distances,
like done by the NSGA-II algorithm (Deb, 2002).
References:
Sean Luke, 2013, Essentials of Metaheuristics, Lulu, second edition
available for free at http://cs.gmu.edu/~sean/book/metaheuristics/
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan (2002).
A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.
"""
def __init__(
self,
problem: Problem,
*,
operators: Iterable,
popsize: int,
elitist: bool = True,
re_evaluate: bool = True,
re_evaluate_parents_first: Optional[bool] = None,
_allow_empty_operator_list: bool = False,
):
"""
`__init__(...)`: Initialize the GeneticAlgorithm.
Args:
problem: The problem to optimize.
operators: Operators to be used by the genetic algorithm.
Expected as an iterable, such as a list or a tuple.
Each item within this iterable object is expected either
as an instance of [Operator][evotorch.operators.base.Operator],
or as a function which receives the decision values of
multiple solutions in a PyTorch tensor (or in an
[ObjectArray][evotorch.tools.objectarray.ObjectArray]
for when dtype is `object`) and returns a modified copy.
popsize: Population size.
elitist: Whether or not this genetic algorithm will behave in an
elitist manner. This argument controls how the genetic
algorithm will form the next generation from the parents
and the children. In elitist mode (i.e. with `elitist=True`),
the procedure to be followed by this genetic algorithm is:
(i) form an extended population which consists of
both the parents and the children,
(ii) sort the extended population from best to worst,
(iii) select the best `n` solutions as the new generation where
`n` is `popsize`.
In non-elitist mode (i.e. with `elitist=False`), the worst `m`
solutions within the parent population are replaced with
the children, `m` being the number of produced children.
re_evaluate: Whether or not to evaluate the solutions
that were already evaluated in the previous generations.
By default, this is set as True.
The reason behind this default setting is that,
in problems where the evaluation procedure is noisy,
by re-evaluating the already-evaluated solutions,
we prevent the bad solutions that were luckily evaluated
from hanging onto the population.
Instead, at every generation, each solution must go through
the evaluation procedure again and prove their worth.
For problems whose evaluation procedures are NOT noisy,
the user might consider turning re_evaluate to False
for saving computational cycles.
re_evaluate_parents_first: This is to be specified only when
`re_evaluate` is True (otherwise to be left as None).
If this is given as True, then it will be assumed that the
provided operators require the parents to be evaluated.
If this is given as False, then it will be assumed that the
provided operators work without looking at the parents'
fitnesses (in which case both parents and children can be
evaluated in a single vectorized computation cycle).
If this is left as None, then whether or not the operators
need to know the parent evaluations will be determined
automatically as follows:
if the operators contain at least one cross-over operator
then `re_evaluate_parents_first` will be internally set as
True; otherwise `re_evaluate_parents_first` will be internally
set as False.
"""
SearchAlgorithm.__init__(self, problem)
self._popsize = int(popsize)
self._elitist: bool = bool(elitist)
self._population = problem.generate_batch(self._popsize)
ExtendedPopulationMixin.__init__(
self,
re_evaluate=re_evaluate,
re_evaluate_parents_first=re_evaluate_parents_first,
operators=operators,
allow_empty_operators_list=_allow_empty_operator_list,
)
SinglePopulationAlgorithmMixin.__init__(self)
@property
def population(self) -> SolutionBatch:
"""Get the population"""
return self._population
def _step(self):
# Get the population size
popsize = self._popsize
if self._elitist:
# This is where we handle the elitist mode.
# Produce and get an extended population in a single SolutionBatch
extended_population = self._make_extended_population(split=False)
# From the extended population, take the best n solutions, n being the popsize.
self._population = extended_population.take_best(popsize)
else:
# This is where we handle the non-elitist mode.
# Take the parent solutions (ensured to be evaluated) and the children separately.
parents, children = self._make_extended_population(split=True)
# Get the number of children
num_children = len(children)
if num_children < popsize:
# If the number of children is less than the population size, then we keep the best m solutions from
# the parents, m being `popsize - num_children`.
chosen_parents = self._population.take_best(popsize - num_children)
# Combine the children with the chosen parents, and declare them as the new population.
self._population = SolutionBatch.cat([chosen_parents, children])
elif num_children == popsize:
# If the number of children is the same with the population size, then these children are declared as
# the new population.
self._population = children
else:
# If the number of children is more than the population size, then we take the best n solutions from
# these children, n being the population size. These chosen children are then declared as the new
# population.
self._population = children.take_best(self._popsize)
population: SolutionBatch
property
readonly
¶
Get the population
__init__(self, problem, *, operators, popsize, elitist=True, re_evaluate=True, re_evaluate_parents_first=None, _allow_empty_operator_list=False)
special
¶
__init__(...): Initialize the GeneticAlgorithm.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
problem |
Problem |
The problem to optimize. |
required |
operators |
Iterable |
Operators to be used by the genetic algorithm.
Expected as an iterable, such as a list or a tuple.
Each item within this iterable object is expected either
as an instance of Operator,
or as a function which receives the decision values of
multiple solutions in a PyTorch tensor (or in an
ObjectArray
for when dtype is |
required |
popsize |
int |
Population size. |
required |
elitist |
bool |
Whether or not this genetic algorithm will behave in an
elitist manner. This argument controls how the genetic
algorithm will form the next generation from the parents
and the children. In elitist mode (i.e. with |
True |
re_evaluate |
bool |
Whether or not to evaluate the solutions that were already evaluated in the previous generations. By default, this is set as True. The reason behind this default setting is that, in problems where the evaluation procedure is noisy, by re-evaluating the already-evaluated solutions, we prevent the bad solutions that were luckily evaluated from hanging onto the population. Instead, at every generation, each solution must go through the evaluation procedure again and prove their worth. For problems whose evaluation procedures are NOT noisy, the user might consider turning re_evaluate to False for saving computational cycles. |
True |
re_evaluate_parents_first |
Optional[bool] |
This is to be specified only when
|
None |
Source code in evotorch/algorithms/ga.py
def __init__(
self,
problem: Problem,
*,
operators: Iterable,
popsize: int,
elitist: bool = True,
re_evaluate: bool = True,
re_evaluate_parents_first: Optional[bool] = None,
_allow_empty_operator_list: bool = False,
):
"""
`__init__(...)`: Initialize the GeneticAlgorithm.
Args:
problem: The problem to optimize.
operators: Operators to be used by the genetic algorithm.
Expected as an iterable, such as a list or a tuple.
Each item within this iterable object is expected either
as an instance of [Operator][evotorch.operators.base.Operator],
or as a function which receives the decision values of
multiple solutions in a PyTorch tensor (or in an
[ObjectArray][evotorch.tools.objectarray.ObjectArray]
for when dtype is `object`) and returns a modified copy.
popsize: Population size.
elitist: Whether or not this genetic algorithm will behave in an
elitist manner. This argument controls how the genetic
algorithm will form the next generation from the parents
and the children. In elitist mode (i.e. with `elitist=True`),
the procedure to be followed by this genetic algorithm is:
(i) form an extended population which consists of
both the parents and the children,
(ii) sort the extended population from best to worst,
(iii) select the best `n` solutions as the new generation where
`n` is `popsize`.
In non-elitist mode (i.e. with `elitist=False`), the worst `m`
solutions within the parent population are replaced with
the children, `m` being the number of produced children.
re_evaluate: Whether or not to evaluate the solutions
that were already evaluated in the previous generations.
By default, this is set as True.
The reason behind this default setting is that,
in problems where the evaluation procedure is noisy,
by re-evaluating the already-evaluated solutions,
we prevent the bad solutions that were luckily evaluated
from hanging onto the population.
Instead, at every generation, each solution must go through
the evaluation procedure again and prove their worth.
For problems whose evaluation procedures are NOT noisy,
the user might consider turning re_evaluate to False
for saving computational cycles.
re_evaluate_parents_first: This is to be specified only when
`re_evaluate` is True (otherwise to be left as None).
If this is given as True, then it will be assumed that the
provided operators require the parents to be evaluated.
If this is given as False, then it will be assumed that the
provided operators work without looking at the parents'
fitnesses (in which case both parents and children can be
evaluated in a single vectorized computation cycle).
If this is left as None, then whether or not the operators
need to know the parent evaluations will be determined
automatically as follows:
if the operators contain at least one cross-over operator
then `re_evaluate_parents_first` will be internally set as
True; otherwise `re_evaluate_parents_first` will be internally
set as False.
"""
SearchAlgorithm.__init__(self, problem)
self._popsize = int(popsize)
self._elitist: bool = bool(elitist)
self._population = problem.generate_batch(self._popsize)
ExtendedPopulationMixin.__init__(
self,
re_evaluate=re_evaluate,
re_evaluate_parents_first=re_evaluate_parents_first,
operators=operators,
allow_empty_operators_list=_allow_empty_operator_list,
)
SinglePopulationAlgorithmMixin.__init__(self)
SteadyStateGA (GeneticAlgorithm)
¶
Thin wrapper around GeneticAlgorithm for compatibility with old code.
This SteadyStateGA class is equivalent to
GeneticAlgorithm except that
SteadyStateGA provides an additional method named use(...) for
specifying a cross-over and/or a mutation operator.
The method use(...) exists only for API compatibility with the previous
versions of EvoTorch. It is recommended to specify the operators via
the keyword argument operators instead.
Source code in evotorch/algorithms/ga.py
class SteadyStateGA(GeneticAlgorithm):
"""
Thin wrapper around GeneticAlgorithm for compatibility with old code.
This `SteadyStateGA` class is equivalent to
[GeneticAlgorithm][evotorch.algorithms.ga.GeneticAlgorithm] except that
`SteadyStateGA` provides an additional method named `use(...)` for
specifying a cross-over and/or a mutation operator.
The method `use(...)` exists only for API compatibility with the previous
versions of EvoTorch. It is recommended to specify the operators via
the keyword argument `operators` instead.
"""
def __init__(
self,
problem: Problem,
*,
popsize: int,
operators: Optional[Iterable] = None,
elitist: bool = True,
re_evaluate: bool = True,
re_evaluate_parents_first: Optional[bool] = None,
):
"""
`__init__(...)`: Initialize the SteadyStateGA.
Args:
problem: The problem to optimize.
operators: Optionally, an iterable of operators to be used by the
genetic algorithm. Each item within this iterable object is
expected either as an instance of
[Operator][evotorch.operators.base.Operator],
or as a function which receives the decision values of
multiple solutions in a PyTorch tensor (or in an
[ObjectArray][evotorch.tools.objectarray.ObjectArray]
for when dtype is `object`) and returns a modified copy.
If this is omitted, then it will be required to specify the
operators via the `use(...)` method.
popsize: Population size.
elitist: Whether or not this genetic algorithm will behave in an
elitist manner. This argument controls how the genetic
algorithm will form the next generation from the parents
and the children. In elitist mode (i.e. with `elitist=True`),
the procedure to be followed by this genetic algorithm is:
(i) form an extended population which consists of
both the parents and the children,
(ii) sort the extended population from best to worst,
(iii) select the best `n` solutions as the new generation where
`n` is `popsize`.
In non-elitist mode (i.e. with `elitist=False`), the worst `m`
solutions within the parent population are replaced with
the children, `m` being the number of produced children.
re_evaluate: Whether or not to evaluate the solutions
that were already evaluated in the previous generations.
By default, this is set as True.
The reason behind this default setting is that,
in problems where the evaluation procedure is noisy,
by re-evaluating the already-evaluated solutions,
we prevent the bad solutions that were luckily evaluated
from hanging onto the population.
Instead, at every generation, each solution must go through
the evaluation procedure again and prove their worth.
For problems whose evaluation procedures are NOT noisy,
the user might consider turning re_evaluate to False
for saving computational cycles.
re_evaluate_parents_first: This is to be specified only when
`re_evaluate` is True (otherwise to be left as None).
If this is given as True, then it will be assumed that the
provided operators require the parents to be evaluated.
If this is given as False, then it will be assumed that the
provided operators work without looking at the parents'
fitnesses (in which case both parents and children can be
evaluated in a single vectorized computation cycle).
If this is left as None, then whether or not the operators
need to know the parent evaluations will be determined
automatically as follows:
if the operators contain at least one cross-over operator
then `re_evaluate_parents_first` will be internally set as
True; otherwise `re_evaluate_parents_first` will be internally
set as False.
Additional note specific to `SteadyStateGA`: if the argument
`operators` is not given (or is given as an empty list), and
also `re_evaluate_parents_first` is left as None, then
`SteadyStateGA` will assume that the operators will be later
given via the `use(...)` method, and that these operators will
require the parents to be evaluated first (equivalent to
setting `re_evaluate_parents_first` as True).
"""
if operators is None:
operators = []
self._cross_over_op: Optional[Callable] = None
self._mutation_op: Optional[Callable] = None
self._forbid_use_method: bool = False
self._prepare_ops: bool = False
if (len(operators) == 0) and re_evaluate and (re_evaluate_parents_first is None):
re_evaluate_parents_first = True
super().__init__(
problem,
operators=operators,
popsize=popsize,
elitist=elitist,
re_evaluate=re_evaluate,
re_evaluate_parents_first=re_evaluate_parents_first,
_allow_empty_operator_list=True,
)
def use(self, operator: Callable):
"""
Specify the cross-over or the mutation operator to use.
This method exists for compatibility with previous EvoTorch code.
Instead of using this method, it is recommended to specify the
operators via the `operators` keyword argument while initializing
this class.
Using this method, one can specify one cross-over operator and one
mutation operator that will be used during the evolutionary search.
Specifying multiple cross-over operators or multiple mutation operators
is not allowed. When the cross-over and mutation operators are
specified via `use(...)`, the order of execution will always be
arranged such that the cross-over comes first and the mutation comes
comes second. If desired, one can specify only the cross-over operator
or only the mutation operator.
Please note that the `operators` keyword argument works differently,
and offers more flexibility for defining the procedure to follow at
each generation. In more details, the `operators` keyword argument
allows one to specify multiple cross-over and/or multiple mutation
operators, and those operators will be executed in the specified
order.
Args:
operator: The operator to be registered to SteadyStateGA.
If the specified operator is cross-over (i.e. an instance
of [CrossOver][evotorch.operators.base.CrossOver]),
then this operator will be registered for the cross-over
phase. If the specified operator is an operator that is
not of the cross-over type (i.e. any instance of
[Operator][evotorch.operators.base.Operator] that is not
[CrossOver][evotorch.operators.base.CrossOver]) or if it is
just a function which receives the decision values as a PyTorch
tensor (or, in the case where `dtype` of the problem is
`object` as an instance of
[ObjectArray][evotorch.tools.objectarray.ObjectArray]) and
returns a modified copy, then that operator will be registered
for the mutation phase of the genetic algorithm.
"""
if self._forbid_use_method:
raise RuntimeError(
"The method `use(...)` cannot be called anymore, because the evolutionary search has started."
)
if len(self._operators) > 0:
raise RuntimeError(
f"The method `use(...)` cannot be called"
f" because an operator list was provided while initializing this {type(self).__name__} instance."
)
if isinstance(operator, CrossOver):
if self._cross_over_op is not None:
raise ValueError(
f"The method `use(...)` received this cross-over operator as its argument:"
f" {operator} (of type {type(operator)})."
f" However, a cross-over operator was already set:"
f" {self._cross_over_op} (of type {type(self._cross_over_op)})."
)
self._cross_over_op = operator
self._prepare_ops = True
else:
if self._mutation_op is not None:
raise ValueError(
f"The method `use(...)` received this mutation operator as its argument:"
f" {operator} (of type {type(operator)})."
f" However, a mutation operator was already set:"
f" {self._mutation_op} (of type {type(self._mutation_op)})."
)
self._mutation_op = operator
self._prepare_ops = True
def _step(self):
self._forbid_use_method = True
if self._prepare_ops:
self._prepare_ops = False
if self._cross_over_op is not None:
self._operators.append(self._cross_over_op)
if self._mutation_op is not None:
self._operators.append(self._mutation_op)
else:
if len(self._operators) == 0:
raise RuntimeError(
f"This {type(self).__name__} instance does not know how to proceed, "
f" because neither the `operators` keyword argument was used during initialization"
f" nor was the `use(...)` method called later."
)
super()._step()
__init__(self, problem, *, popsize, operators=None, elitist=True, re_evaluate=True, re_evaluate_parents_first=None)
special
¶
__init__(...): Initialize the SteadyStateGA.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
problem |
Problem |
The problem to optimize. |
required |
operators |
Optional[Iterable] |
Optionally, an iterable of operators to be used by the
genetic algorithm. Each item within this iterable object is
expected either as an instance of
Operator,
or as a function which receives the decision values of
multiple solutions in a PyTorch tensor (or in an
ObjectArray
for when dtype is |
None |
popsize |
int |
Population size. |
required |
elitist |
bool |
Whether or not this genetic algorithm will behave in an
elitist manner. This argument controls how the genetic
algorithm will form the next generation from the parents
and the children. In elitist mode (i.e. with |
True |
re_evaluate |
bool |
Whether or not to evaluate the solutions that were already evaluated in the previous generations. By default, this is set as True. The reason behind this default setting is that, in problems where the evaluation procedure is noisy, by re-evaluating the already-evaluated solutions, we prevent the bad solutions that were luckily evaluated from hanging onto the population. Instead, at every generation, each solution must go through the evaluation procedure again and prove their worth. For problems whose evaluation procedures are NOT noisy, the user might consider turning re_evaluate to False for saving computational cycles. |
True |
re_evaluate_parents_first |
Optional[bool] |
This is to be specified only when
|
None |
Source code in evotorch/algorithms/ga.py
def __init__(
self,
problem: Problem,
*,
popsize: int,
operators: Optional[Iterable] = None,
elitist: bool = True,
re_evaluate: bool = True,
re_evaluate_parents_first: Optional[bool] = None,
):
"""
`__init__(...)`: Initialize the SteadyStateGA.
Args:
problem: The problem to optimize.
operators: Optionally, an iterable of operators to be used by the
genetic algorithm. Each item within this iterable object is
expected either as an instance of
[Operator][evotorch.operators.base.Operator],
or as a function which receives the decision values of
multiple solutions in a PyTorch tensor (or in an
[ObjectArray][evotorch.tools.objectarray.ObjectArray]
for when dtype is `object`) and returns a modified copy.
If this is omitted, then it will be required to specify the
operators via the `use(...)` method.
popsize: Population size.
elitist: Whether or not this genetic algorithm will behave in an
elitist manner. This argument controls how the genetic
algorithm will form the next generation from the parents
and the children. In elitist mode (i.e. with `elitist=True`),
the procedure to be followed by this genetic algorithm is:
(i) form an extended population which consists of
both the parents and the children,
(ii) sort the extended population from best to worst,
(iii) select the best `n` solutions as the new generation where
`n` is `popsize`.
In non-elitist mode (i.e. with `elitist=False`), the worst `m`
solutions within the parent population are replaced with
the children, `m` being the number of produced children.
re_evaluate: Whether or not to evaluate the solutions
that were already evaluated in the previous generations.
By default, this is set as True.
The reason behind this default setting is that,
in problems where the evaluation procedure is noisy,
by re-evaluating the already-evaluated solutions,
we prevent the bad solutions that were luckily evaluated
from hanging onto the population.
Instead, at every generation, each solution must go through
the evaluation procedure again and prove their worth.
For problems whose evaluation procedures are NOT noisy,
the user might consider turning re_evaluate to False
for saving computational cycles.
re_evaluate_parents_first: This is to be specified only when
`re_evaluate` is True (otherwise to be left as None).
If this is given as True, then it will be assumed that the
provided operators require the parents to be evaluated.
If this is given as False, then it will be assumed that the
provided operators work without looking at the parents'
fitnesses (in which case both parents and children can be
evaluated in a single vectorized computation cycle).
If this is left as None, then whether or not the operators
need to know the parent evaluations will be determined
automatically as follows:
if the operators contain at least one cross-over operator
then `re_evaluate_parents_first` will be internally set as
True; otherwise `re_evaluate_parents_first` will be internally
set as False.
Additional note specific to `SteadyStateGA`: if the argument
`operators` is not given (or is given as an empty list), and
also `re_evaluate_parents_first` is left as None, then
`SteadyStateGA` will assume that the operators will be later
given via the `use(...)` method, and that these operators will
require the parents to be evaluated first (equivalent to
setting `re_evaluate_parents_first` as True).
"""
if operators is None:
operators = []
self._cross_over_op: Optional[Callable] = None
self._mutation_op: Optional[Callable] = None
self._forbid_use_method: bool = False
self._prepare_ops: bool = False
if (len(operators) == 0) and re_evaluate and (re_evaluate_parents_first is None):
re_evaluate_parents_first = True
super().__init__(
problem,
operators=operators,
popsize=popsize,
elitist=elitist,
re_evaluate=re_evaluate,
re_evaluate_parents_first=re_evaluate_parents_first,
_allow_empty_operator_list=True,
)
use(self, operator)
¶
Specify the cross-over or the mutation operator to use.
This method exists for compatibility with previous EvoTorch code.
Instead of using this method, it is recommended to specify the
operators via the operators keyword argument while initializing
this class.
Using this method, one can specify one cross-over operator and one
mutation operator that will be used during the evolutionary search.
Specifying multiple cross-over operators or multiple mutation operators
is not allowed. When the cross-over and mutation operators are
specified via use(...), the order of execution will always be
arranged such that the cross-over comes first and the mutation comes
comes second. If desired, one can specify only the cross-over operator
or only the mutation operator.
Please note that the operators keyword argument works differently,
and offers more flexibility for defining the procedure to follow at
each generation. In more details, the operators keyword argument
allows one to specify multiple cross-over and/or multiple mutation
operators, and those operators will be executed in the specified
order.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
operator |
Callable |
The operator to be registered to SteadyStateGA.
If the specified operator is cross-over (i.e. an instance
of CrossOver),
then this operator will be registered for the cross-over
phase. If the specified operator is an operator that is
not of the cross-over type (i.e. any instance of
Operator that is not
CrossOver) or if it is
just a function which receives the decision values as a PyTorch
tensor (or, in the case where |
required |
Source code in evotorch/algorithms/ga.py
def use(self, operator: Callable):
"""
Specify the cross-over or the mutation operator to use.
This method exists for compatibility with previous EvoTorch code.
Instead of using this method, it is recommended to specify the
operators via the `operators` keyword argument while initializing
this class.
Using this method, one can specify one cross-over operator and one
mutation operator that will be used during the evolutionary search.
Specifying multiple cross-over operators or multiple mutation operators
is not allowed. When the cross-over and mutation operators are
specified via `use(...)`, the order of execution will always be
arranged such that the cross-over comes first and the mutation comes
comes second. If desired, one can specify only the cross-over operator
or only the mutation operator.
Please note that the `operators` keyword argument works differently,
and offers more flexibility for defining the procedure to follow at
each generation. In more details, the `operators` keyword argument
allows one to specify multiple cross-over and/or multiple mutation
operators, and those operators will be executed in the specified
order.
Args:
operator: The operator to be registered to SteadyStateGA.
If the specified operator is cross-over (i.e. an instance
of [CrossOver][evotorch.operators.base.CrossOver]),
then this operator will be registered for the cross-over
phase. If the specified operator is an operator that is
not of the cross-over type (i.e. any instance of
[Operator][evotorch.operators.base.Operator] that is not
[CrossOver][evotorch.operators.base.CrossOver]) or if it is
just a function which receives the decision values as a PyTorch
tensor (or, in the case where `dtype` of the problem is
`object` as an instance of
[ObjectArray][evotorch.tools.objectarray.ObjectArray]) and
returns a modified copy, then that operator will be registered
for the mutation phase of the genetic algorithm.
"""
if self._forbid_use_method:
raise RuntimeError(
"The method `use(...)` cannot be called anymore, because the evolutionary search has started."
)
if len(self._operators) > 0:
raise RuntimeError(
f"The method `use(...)` cannot be called"
f" because an operator list was provided while initializing this {type(self).__name__} instance."
)
if isinstance(operator, CrossOver):
if self._cross_over_op is not None:
raise ValueError(
f"The method `use(...)` received this cross-over operator as its argument:"
f" {operator} (of type {type(operator)})."
f" However, a cross-over operator was already set:"
f" {self._cross_over_op} (of type {type(self._cross_over_op)})."
)
self._cross_over_op = operator
self._prepare_ops = True
else:
if self._mutation_op is not None:
raise ValueError(
f"The method `use(...)` received this mutation operator as its argument:"
f" {operator} (of type {type(operator)})."
f" However, a mutation operator was already set:"
f" {self._mutation_op} (of type {type(self._mutation_op)})."
)
self._mutation_op = operator
self._prepare_ops = True