Mutation Operators

The pycellga.mutation module provides a comprehensive set of mutation operators designed to introduce variation into the population during the genetic algorithm optimization process. Each operator serves a unique purpose based on the representation of the chromosome (binary, real-valued, or permutation-based) and the specific requirements of the optimization problem.

This module includes the following mutation operators:

Bit Flip Mutation

Applies a bitwise flip to binary-encoded chromosomes. This operator is a classic choice for binary genetic algorithms, offering a simple yet effective mutation approach.

class BitFlipMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

BitFlipMutation performs a bit flip mutation on an individual in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the BitFlipMutation object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform a bit flip mutation on the candidate individual.

A single bit in the candidate’s chromosome is randomly selected and flipped (i.e., a 0 is changed to a 1, or a 1 is changed to a 0).

Returns:: A new individual with the mutated chromosome.
Return type:: Individual

Byte-Level Mutation

Performs mutations at the byte level for real-valued chromosomes. This operator leverages byte manipulation to create small, precise adjustments in the solution space, optimizing the algorithm’s performance for continuous functions.

class ByteMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

ByteMutation operator defined in (Satman, 2013). ByteMutation performs a byte-wise mutation on an individual’s chromosome in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the ByteMutation object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform a byte-wise mutation on the candidate individual.

A single byte in one of the candidate’s chromosome’s floating-point numbers is randomly selected and either incremented or decremented by 1, wrapping around if necessary.

Returns:: A new individual with the mutated chromosome.
Return type:: Individual

Randomized Byte Mutation

Introduces randomness at the byte level, enabling broader exploration in real-valued optimization tasks. This operator is particularly effective when a high degree of variation is desirable.

class ByteMutationRandom(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

ByteMutationRandom operator defined in (Satman, 2013). ByteMutationRandom performs a random byte mutation on an individual’s chromosome in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the ByteMutationRandom object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform a random byte mutation on the candidate individual.

A single byte in one of the candidate’s chromosome’s floating-point numbers is randomly selected and mutated to a random value.

Returns:: A new individual with the mutated chromosome.
Return type:: Individual

Uniform Float Mutation

Applies uniform random mutations across real-valued chromosomes. This operator is suitable for continuous optimization, where each gene is adjusted within a defined range to enhance solution diversity.

class FloatUniformMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

FloatUniformMutation performs a uniform mutation on an individual’s chromosome in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the FloatUniformMutation object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform a uniform mutation on the candidate individual.

Each gene in the candidate’s chromosome is mutated by adding or subtracting a random float uniformly sampled from [0, 1]. The mutation is rounded to 5 decimal places.

Returns:: A new individual with the mutated chromosome.
Return type:: Individual

Insertion-Based Mutation

A mutation strategy tailored for permutation-based representations, such as in sequencing and scheduling problems. This operator repositions a randomly selected gene within the chromosome, altering the order while preserving elements.

class InsertionMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

InsertionMutation performs an insertion mutation on an individual’s chromosome in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the InsertionMutation object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform an insertion mutation on the candidate individual.

A gene in the candidate’s chromosome is randomly selected and moved to a new position in the chromosome.

Returns:: A new individual with the mutated chromosome.
Return type:: Individual

Shuffle Mutation

Randomly rearranges a subset of genes in the chromosome. This operator is effective in permutation-based problems, promoting diversity by shuffling segments without altering individual gene values.

class ShuffleMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

ShuffleMutation performs a shuffle mutation on an individual’s chromosome in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the ShuffleMutation object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform a shuffle mutation on the candidate individual.

A subsequence of genes in the candidate’s chromosome is randomly selected and shuffled.

Returns:: A new individual with the mutated chromosome.
Return type:: Individual

Swap Mutation

Swaps the positions of two genes, introducing subtle changes ideal for permutation-based optimizations. This operator is commonly applied in combinatorial problems where order is significant.

class SwapMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

SwapMutation performs a swap mutation on an individual’s chromosome in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the SwapMutation object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform a swap mutation on the candidate individual.

Two genes in the candidate’s chromosome are randomly selected and swapped.

Returns:: A new individual with the mutated chromosome.
Return type:: Individual

Two-Opt Mutation

A mutation operator frequently used in path optimization problems, such as the Traveling Salesman Problem. It reverses a segment of the chromosome, allowing for new path configurations without altering the gene order.

class TwoOptMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Bases: MutationOperator

TwoOptMutation performs a 2-opt mutation on an individual’s chromosome in a Genetic Algorithm.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]

Initialize the TwoOptMutation object.

Parameters:

mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).

mutate() → Individual[source]

Perform a 2-opt mutation on the candidate individual.

A segment of the candidate’s chromosome is randomly selected and reversed.

Returns:: A new individual with the mutated chromosome.
Return type:: Individual