Permutation-encoded genetic algorithm#
-
namespace gapp
class PermutationGene#
#include <encoding/permutation.hpp>
class GaTraits<PermutationGene>#
#include <encoding/permutation.hpp>
class PermutationGA#
#include <encoding/permutation.hpp>
-
class PermutationGA : public gapp::GA<PermutationGene>#
Permutation-encoded genetic algorithm class. This is the main solver that should be used for combinatorial problems.
The chromosome of a candidate solution encodes a permutation. Every gene of a chromosome is a unique unsigned integer in the closed interval of [0, chrom_len - 1],
Without any loss of generality, the first and last elements of the permutations are assumed to be unrelated, eg. the permutation A-B-C-D will not be considered equal to the permutation B-C-D-A by the GA. The fitness function should also be written with this in mind.
Public Functions
-
GA(Positive<size_t> population_size = DEFAULT_POPSIZE)#
Create a genetic algorithm using the default genetic operators.
The algorithm used will be deduced from the number of objectives of the fitness function (single- or multi-objective), along with the mutation probability used, which will be deduced from the chromosome length.
- Parameters:
population_size – The number of candidates in the population. Must be at least 1.
- GA( )#
Create a genetic algorithm using the default genetic operators. The mutation probability used will be deduced from the chromosome length.
- Parameters:
population_size – The number of candidates in the population. Must be at least 1.
algorithm – The algorithm to use. The default algorithm will be used if it’s a nullptr.
- GA(
- Positive<size_t> population_size,
- std::unique_ptr<crossover::Crossover<T>> crossover,
- std::unique_ptr<mutation::Mutation<T>> mutation,
- std::unique_ptr<stopping::StopCondition> stop_condition = std::make_unique<stopping::NoEarlyStop>(),
Create a genetic algorithm using the specified operators. The algorithm used will be deduced from the number of objectives of the fitness function (single- or multi-objective).
- Parameters:
population_size – The number of candidates in the population. Must be at least 1.
crossover – The crossover operator to use. Can’t be a nullptr.
mutation – The mutation operator to use. Can’t be a nullptr.
stop_condition – The early-stop condition to use. No early-stopping will be used if it’s a nullptr.
- GA(
- Positive<size_t> population_size,
- std::unique_ptr<algorithm::Algorithm> algorithm,
- std::unique_ptr<crossover::Crossover<T>> crossover,
- std::unique_ptr<mutation::Mutation<T>> mutation,
- std::unique_ptr<stopping::StopCondition> stop_condition = std::make_unique<stopping::NoEarlyStop>(),
Create a genetic algorithm using the specified algorithm and operators.
- Parameters:
population_size – The number of candidates in the population. Must be at least 1.
algorithm – The algorithm to use. The default algorithm will be used if it’s a nullptr.
crossover – The crossover operator to use. Can’t be a nullptr.
mutation – The mutation operator to use. Can’t be a nullptr.
stop_condition – The early-stop condition to use. No early-stopping will be used if it’s a nullptr.
-
template<typename AlgorithmType>
GA( - Positive<size_t> population_size,
- AlgorithmType algorithm,
Create a genetic algorithm using the default genetic operators. The mutation probability will be deduced from the chromosome length.
- Parameters:
population_size – The number of candidates in the population. Must be at least 1.
algorithm – The algorithm to use.
-
template<typename CrossoverType, typename MutationType, typename StoppingType = stopping::NoEarlyStop>
GA( - Positive<size_t> population_size,
- CrossoverType crossover,
- MutationType mutation,
- StoppingType stop_condition = {},
Create a genetic algorithm using the specified operators. The algorithm used will be deduced from the number of objectives of the fitness function (single- or multi-objective).
- Parameters:
population_size – The number of candidates in the population. Must be at least 1.
crossover – The crossover operator to use.
mutation – The mutation operator to use.
stop_condition – The early-stop condition to use.
-
template<typename AlgorithmType, typename CrossoverType, typename MutationType, typename StoppingType = stopping::NoEarlyStop>
GA( - Positive<size_t> population_size,
- AlgorithmType algorithm,
- CrossoverType crossover,
- MutationType mutation,
- StoppingType stop_condition = {},
Create a genetic algorithm using the specified algorithm and operators.
- Parameters:
population_size – The number of candidates in the population. Must be at least 1.
algorithm – The algorithm to use.
crossover – The crossover operator to use.
mutation – The mutation operator to use.
stop_condition – The early-stop condition to use.
-
GA(Positive<size_t> population_size = DEFAULT_POPSIZE)#