Genetic Algorithms

Technique

Genetic Algorithms (GAs) are a class of optimization and search heuristics inspired by the principle of natural selection. They are particularly useful for combinatorial problems with large search spaces where no tractable algorithm is available, and they have been applied across software testing, default reasoning, and automated program generation.

First seen 6/27/2026

Last seen 6/27/2026

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Overview

Genetic Algorithms (GAs) are a family of population-based search and optimization techniques inspired by the biological process of natural selection. They operate by evolving a population of candidate solutions through selection, crossover, and mutation operators, favoring individuals with higher fitness over successive generations. GAs are especially suited to problems characterized by huge search spaces and the absence of tractable exact algorithms.

Characteristics

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LINKED ENTITIES

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Evolutionary Algorithms is_a The metamorphic-testing paper explicitly refers to relations that generalize 'across different types of evolutionary algorithms,' indicating GAs are a subtype of evolutionary algorithms.

proGenesys instantiated_by proGenesys is a program generation tool explicitly built on genetic-system principles, presented at ICAI 1999.

CITATIONS

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[1] Genetic Algorithms are inspired by the principle of natural selection and have been applied to combinatorial problems with huge search spaces where no tractable algorithm is available. Genetic Algorithms for Extension Search in Default Logic

[2] Statistical metamorphic testing has been applied to Genetic Algorithms by identifying 17 metamorphic relations, which are more effective at finding defects than traditional unit tests based on known outputs. Systematic Testing of Genetic Algorithms: A Metamorphic Testing based Approach

[3] Three system-level metamorphic relations for GAs were found to fail excessively across fitness functions and were subsequently modified to reduce their failure rates. Systematic Testing of Genetic Algorithms: A Metamorphic Testing based Approach

[4] Some metamorphic relations identified for Genetic Algorithms are generalizable across different types of evolutionary algorithms, and produced similar mutation scores between two implementations. Systematic Testing of Genetic Algorithms: A Metamorphic Testing based Approach

[5] GAs have been used to build an efficient default reasoning system, with a formal description of the components required for an extension search based on GA principles and supporting experimental results. Genetic Algorithms for Extension Search in Default Logic

[6] proGenesys is a program generation tool based on genetic-system principles, presented at the 1999 International Conference on Artificial Intelligence, with applications including embedded systems design. proGenesys-Program Generation Tool Based on Genetic Systems

[7] GAs are inherently stochastic in initialization, selection, crossover, and mutation, leading to non-deterministic outputs for the same input. Systematic Testing of Genetic Algorithms: A Metamorphic Testing based Approach