Genetic algorithm - Revision history

PeterGrant07: /* Chromosome representation */ adding early innovations in variable-length representations

2025-05-24T21:33:33Z

Chromosome representation: adding early innovations in variable-length representations

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	An expansion of the Genetic Algorithm accessible problem domain can be obtained through more complex encoding of the solution pools by concatenating several types of heterogenously encoded genes into one chromosome.<ref name=Patrascu2014>{{cite journal\|last1=Patrascu\|first1=M.\|last2=Stancu\|first2=A.F.\|last3=Pop\|first3=F.\|title=HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation\|journal=Soft Computing\|year=2014\|volume=18\|issue=12\|pages=2565–2576\|doi=10.1007/s00500-014-1401-y\|s2cid=29821873}}</ref> This particular approach allows for solving optimization problems that require vastly disparate definition domains for the problem parameters. For instance, in problems of cascaded controller tuning, the internal loop controller structure can belong to a conventional regulator of three parameters, whereas the external loop could implement a linguistic controller (such as a fuzzy system) which has an inherently different description. This particular form of encoding requires a specialized crossover mechanism that recombines the chromosome by section, and it is a useful tool for the modelling and simulation of complex adaptive systems, especially evolution processes.		An expansion of the Genetic Algorithm accessible problem domain can be obtained through more complex encoding of the solution pools by concatenating several types of heterogenously encoded genes into one chromosome.<ref name=Patrascu2014>{{cite journal\|last1=Patrascu\|first1=M.\|last2=Stancu\|first2=A.F.\|last3=Pop\|first3=F.\|title=HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation\|journal=Soft Computing\|year=2014\|volume=18\|issue=12\|pages=2565–2576\|doi=10.1007/s00500-014-1401-y\|s2cid=29821873}}</ref> This particular approach allows for solving optimization problems that require vastly disparate definition domains for the problem parameters. For instance, in problems of cascaded controller tuning, the internal loop controller structure can belong to a conventional regulator of three parameters, whereas the external loop could implement a linguistic controller (such as a fuzzy system) which has an inherently different description. This particular form of encoding requires a specialized crossover mechanism that recombines the chromosome by section, and it is a useful tool for the modelling and simulation of complex adaptive systems, especially evolution processes.

			Another important expansion of the Genetic Algorithm (GA) accessible solution space was driven by the need to make representations amenable to variable levels of knowledge about the solution states. Variable-length representations were inspired by the observation that, in nature, evolution tends to progress from simpler organisms to more complex ones—suggesting an underlying rationale for embracing flexible structures.<ref>Goldberg, D.E., Korb, B., & Deb, K. (1989). Messy Genetic Algorithms: Motivation, Analysis, and First Results. Complex Systems, 3(5), 493–530. ISSN 0891-2513.</ref> A second, more pragmatic motivation was that most real-world engineering and knowledge-based problems do not naturally conform to rigid knowledge structures.<ref>Davidor, Y. (1991). Genetic Algorithms and Robotics: A Heuristic Strategy for Optimization. World Scientific Series in Robotics and Intelligent Systems: Volume 1.</ref>

			These early innovations in variable-length representations laid essential groundwork for the development of [[Genetic programming]], which further extended the classical GA paradigm. Such representations required enhancements to the simplistic genetic operators used for fixed-length chromosomes, enabling the emergence of more sophisticated and adaptive GA models.

	=== Elitism ===		=== Elitism ===

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	[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]		[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]
	<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->		<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->
	In [[computer science]] and [[operations research]], a '''genetic algorithm (GA)''' is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA).<ref>{{Cite book \|last1=Pétrowski \|first1=Alain \|last2=Ben-Hamida \|first2=Sana \|title=Evolutionary algorithms \|publisher=John Wiley & Sons \|page=30 \|year=2017 \|isbn=978-1-119-13638-5 \|url=https://books.google.com/books?id=GlGpDgAAQBAJ&dq=genetic+algorithm+evolutionary+algorithms&pg=PP2}}</ref> Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s via biologically inspired operators such as [[selection (genetic algorithm)\|selection]], [[crossover (genetic algorithm)\|crossover]], and [[Mutation (genetic algorithm)\|mutation]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], and [[causal inference]].<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref>		In [[computer science]] and [[operations research]], a '''genetic algorithm''' ('''GA''') is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA).<ref>{{Cite book \|last1=Pétrowski \|first1=Alain \|last2=Ben-Hamida \|first2=Sana \|title=Evolutionary algorithms \|publisher=John Wiley & Sons \|page=30 \|year=2017 \|isbn=978-1-119-13638-5 \|url=https://books.google.com/books?id=GlGpDgAAQBAJ&dq=genetic+algorithm+evolutionary+algorithms&pg=PP2}}</ref> Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s via biologically inspired operators such as [[selection (genetic algorithm)\|selection]], [[crossover (genetic algorithm)\|crossover]], and [[Mutation (genetic algorithm)\|mutation]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], and [[causal inference]].<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref>

	== Methodology ==		== Methodology ==

Dominic3203: /* top */

2025-04-13T08:53:37Z

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	Although crossover and mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization-extinction, or migration in genetic algorithms.{{citation needed\|date=November 2019}}		Although crossover and mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization-extinction, or migration in genetic algorithms.{{citation needed\|date=November 2019}}

	It is worth tuning parameters such as the [[Mutation (genetic algorithm)\|mutation]] probability, [[Crossover (genetic algorithm)\|crossover]] probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to [[genetic drift]] (which is non-[[Ergodicity\|ergodic]] in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless [[#Elitism\|elitist selection]] is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required.		It is worth tuning parameters such as the [[Mutation (genetic algorithm)\|mutation]] probability, [[Crossover (genetic algorithm)\|crossover]] probability and population size to find reasonable settings for the problem's [[complexity class]] being worked on. A very small mutation rate may lead to [[genetic drift]] (which is non-[[Ergodicity\|ergodic]] in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless [[#Elitism\|elitist selection]] is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required.

	==== Heuristics ====		==== Heuristics ====
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	:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn\|Goldberg\|1989\|p=41}}		:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn\|Goldberg\|1989\|p=41}}

	Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many [[estimation of distribution algorithm]]s, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold.<ref>{{cite book\|last1=Harik\|first1=Georges R.\|last2=Lobo\|first2=Fernando G.\|last3=Sastry\|first3=Kumara\|title=Scalable Optimization via Probabilistic Modeling \|chapter=Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) \|volume=33\|date=1 January 2006\|pages=39–61\|doi=10.1007/978-3-540-34954-9_3\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-34953-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Goldberg\|first2=David E.\|last3=Cantú-Paz\|first3=Erick\|title=BOA: The Bayesian Optimization Algorithm\|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1\|date=1 January 1999\|pages=525–532\|url=http://dl.acm.org/citation.cfm?id=2933973\|isbn=9781558606111\|series=Gecco'99}}</ref> Although good results have been reported for some classes of ~~problems~~, skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.<ref>{{cite book\|last1=Coffin\|first1=David\|last2=Smith\|first2=Robert E.\|title=Linkage in Evolutionary Computation \|chapter=Linkage Learning in Estimation of Distribution Algorithms \|volume=157\|date=1 January 2008\|pages=141–156\|doi=10.1007/978-3-540-85068-7_7\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-85067-0}}</ref><ref>{{cite journal\|last1=Echegoyen\|first1=Carlos\|last2=Mendiburu\|first2=Alexander\|last3=Santana\|first3=Roberto\|last4=Lozano\|first4=Jose A.\|title=On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms\|journal=Evolutionary Computation\|date=8 November 2012\|volume=21\|issue=3\|pages=471–495\|doi=10.1162/EVCO_a_00095\|pmid=23136917\|s2cid=26585053\|issn=1063-6560}}</ref><ref>{{cite book\|last1=Sadowski\|first1=Krzysztof L.\|last2=Bosman\|first2=Peter A.N.\|last3=Thierens\|first3=Dirk\|title=Proceedings of the 15th annual conference on Genetic and evolutionary computation \|chapter=On the usefulness of linkage processing for solving MAX-SAT \|date=1 January 2013\|pages=853–860\|doi=10.1145/2463372.2463474\|isbn=9781450319638\|series=Gecco '13\|hdl=1874/290291\|s2cid=9986768}}</ref>		Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many [[estimation of distribution algorithm]]s, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold.<ref>{{cite book\|last1=Harik\|first1=Georges R.\|last2=Lobo\|first2=Fernando G.\|last3=Sastry\|first3=Kumara\|title=Scalable Optimization via Probabilistic Modeling \|chapter=Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) \|volume=33\|date=1 January 2006\|pages=39–61\|doi=10.1007/978-3-540-34954-9_3\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-34953-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Goldberg\|first2=David E.\|last3=Cantú-Paz\|first3=Erick\|title=BOA: The Bayesian Optimization Algorithm\|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1\|date=1 January 1999\|pages=525–532\|url=http://dl.acm.org/citation.cfm?id=2933973\|isbn=9781558606111\|series=Gecco'99}}</ref> Although good results have been reported for some [[List of complexity classes\|classes of problem]]s, skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.<ref>{{cite book\|last1=Coffin\|first1=David\|last2=Smith\|first2=Robert E.\|title=Linkage in Evolutionary Computation \|chapter=Linkage Learning in Estimation of Distribution Algorithms \|volume=157\|date=1 January 2008\|pages=141–156\|doi=10.1007/978-3-540-85068-7_7\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-85067-0}}</ref><ref>{{cite journal\|last1=Echegoyen\|first1=Carlos\|last2=Mendiburu\|first2=Alexander\|last3=Santana\|first3=Roberto\|last4=Lozano\|first4=Jose A.\|title=On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms\|journal=Evolutionary Computation\|date=8 November 2012\|volume=21\|issue=3\|pages=471–495\|doi=10.1162/EVCO_a_00095\|pmid=23136917\|s2cid=26585053\|issn=1063-6560}}</ref><ref>{{cite book\|last1=Sadowski\|first1=Krzysztof L.\|last2=Bosman\|first2=Peter A.N.\|last3=Thierens\|first3=Dirk\|title=Proceedings of the 15th annual conference on Genetic and evolutionary computation \|chapter=On the usefulness of linkage processing for solving MAX-SAT \|date=1 January 2013\|pages=853–860\|doi=10.1145/2463372.2463474\|isbn=9781450319638\|series=Gecco '13\|hdl=1874/290291\|s2cid=9986768}}</ref>

	== Limitations ==		== Limitations ==

Dominic3203: Undid revision 1285356342 by Dominic3203 (talk)

2025-04-13T08:50:19Z

Undid revision 1285356342 by Dominic3203 (talk)

← Previous revision		Revision as of 08:50, 13 April 2025
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	:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn\|Goldberg\|1989\|p=41}}		:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn\|Goldberg\|1989\|p=41}}

	Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many [[estimation of distribution algorithm]]s, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold.<ref>{{cite book\|last1=Harik\|first1=Georges R.\|last2=Lobo\|first2=Fernando G.\|last3=Sastry\|first3=Kumara\|title=Scalable Optimization via Probabilistic Modeling \|chapter=Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) \|volume=33\|date=1 January 2006\|pages=39–61\|doi=10.1007/978-3-540-34954-9_3\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-34953-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Goldberg\|first2=David E.\|last3=Cantú-Paz\|first3=Erick\|title=BOA: The Bayesian Optimization Algorithm\|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1\|date=1 January 1999\|pages=525–532\|url=http://dl.acm.org/citation.cfm?id=2933973\|isbn=9781558606111\|series=Gecco'99}}</ref> Although good results have been reported for some ~~[[Time complexity#~~classes of problems]], skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.<ref>{{cite book\|last1=Coffin\|first1=David\|last2=Smith\|first2=Robert E.\|title=Linkage in Evolutionary Computation \|chapter=Linkage Learning in Estimation of Distribution Algorithms \|volume=157\|date=1 January 2008\|pages=141–156\|doi=10.1007/978-3-540-85068-7_7\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-85067-0}}</ref><ref>{{cite journal\|last1=Echegoyen\|first1=Carlos\|last2=Mendiburu\|first2=Alexander\|last3=Santana\|first3=Roberto\|last4=Lozano\|first4=Jose A.\|title=On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms\|journal=Evolutionary Computation\|date=8 November 2012\|volume=21\|issue=3\|pages=471–495\|doi=10.1162/EVCO_a_00095\|pmid=23136917\|s2cid=26585053\|issn=1063-6560}}</ref><ref>{{cite book\|last1=Sadowski\|first1=Krzysztof L.\|last2=Bosman\|first2=Peter A.N.\|last3=Thierens\|first3=Dirk\|title=Proceedings of the 15th annual conference on Genetic and evolutionary computation \|chapter=On the usefulness of linkage processing for solving MAX-SAT \|date=1 January 2013\|pages=853–860\|doi=10.1145/2463372.2463474\|isbn=9781450319638\|series=Gecco '13\|hdl=1874/290291\|s2cid=9986768}}</ref>		Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many [[estimation of distribution algorithm]]s, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold.<ref>{{cite book\|last1=Harik\|first1=Georges R.\|last2=Lobo\|first2=Fernando G.\|last3=Sastry\|first3=Kumara\|title=Scalable Optimization via Probabilistic Modeling \|chapter=Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) \|volume=33\|date=1 January 2006\|pages=39–61\|doi=10.1007/978-3-540-34954-9_3\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-34953-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Goldberg\|first2=David E.\|last3=Cantú-Paz\|first3=Erick\|title=BOA: The Bayesian Optimization Algorithm\|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1\|date=1 January 1999\|pages=525–532\|url=http://dl.acm.org/citation.cfm?id=2933973\|isbn=9781558606111\|series=Gecco'99}}</ref> Although good results have been reported for some classes of problems, skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.<ref>{{cite book\|last1=Coffin\|first1=David\|last2=Smith\|first2=Robert E.\|title=Linkage in Evolutionary Computation \|chapter=Linkage Learning in Estimation of Distribution Algorithms \|volume=157\|date=1 January 2008\|pages=141–156\|doi=10.1007/978-3-540-85068-7_7\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-85067-0}}</ref><ref>{{cite journal\|last1=Echegoyen\|first1=Carlos\|last2=Mendiburu\|first2=Alexander\|last3=Santana\|first3=Roberto\|last4=Lozano\|first4=Jose A.\|title=On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms\|journal=Evolutionary Computation\|date=8 November 2012\|volume=21\|issue=3\|pages=471–495\|doi=10.1162/EVCO_a_00095\|pmid=23136917\|s2cid=26585053\|issn=1063-6560}}</ref><ref>{{cite book\|last1=Sadowski\|first1=Krzysztof L.\|last2=Bosman\|first2=Peter A.N.\|last3=Thierens\|first3=Dirk\|title=Proceedings of the 15th annual conference on Genetic and evolutionary computation \|chapter=On the usefulness of linkage processing for solving MAX-SAT \|date=1 January 2013\|pages=853–860\|doi=10.1145/2463372.2463474\|isbn=9781450319638\|series=Gecco '13\|hdl=1874/290291\|s2cid=9986768}}</ref>

	== Limitations ==		== Limitations ==

Dominic3203: /* top */Added links

2025-04-13T07:02:09Z

top: Added links

← Previous revision		Revision as of 07:02, 13 April 2025
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	:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn\|Goldberg\|1989\|p=41}}		:"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."{{sfn\|Goldberg\|1989\|p=41}}

	Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many [[estimation of distribution algorithm]]s, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold.<ref>{{cite book\|last1=Harik\|first1=Georges R.\|last2=Lobo\|first2=Fernando G.\|last3=Sastry\|first3=Kumara\|title=Scalable Optimization via Probabilistic Modeling \|chapter=Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) \|volume=33\|date=1 January 2006\|pages=39–61\|doi=10.1007/978-3-540-34954-9_3\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-34953-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Goldberg\|first2=David E.\|last3=Cantú-Paz\|first3=Erick\|title=BOA: The Bayesian Optimization Algorithm\|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1\|date=1 January 1999\|pages=525–532\|url=http://dl.acm.org/citation.cfm?id=2933973\|isbn=9781558606111\|series=Gecco'99}}</ref> Although good results have been reported for some classes of problems, skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.<ref>{{cite book\|last1=Coffin\|first1=David\|last2=Smith\|first2=Robert E.\|title=Linkage in Evolutionary Computation \|chapter=Linkage Learning in Estimation of Distribution Algorithms \|volume=157\|date=1 January 2008\|pages=141–156\|doi=10.1007/978-3-540-85068-7_7\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-85067-0}}</ref><ref>{{cite journal\|last1=Echegoyen\|first1=Carlos\|last2=Mendiburu\|first2=Alexander\|last3=Santana\|first3=Roberto\|last4=Lozano\|first4=Jose A.\|title=On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms\|journal=Evolutionary Computation\|date=8 November 2012\|volume=21\|issue=3\|pages=471–495\|doi=10.1162/EVCO_a_00095\|pmid=23136917\|s2cid=26585053\|issn=1063-6560}}</ref><ref>{{cite book\|last1=Sadowski\|first1=Krzysztof L.\|last2=Bosman\|first2=Peter A.N.\|last3=Thierens\|first3=Dirk\|title=Proceedings of the 15th annual conference on Genetic and evolutionary computation \|chapter=On the usefulness of linkage processing for solving MAX-SAT \|date=1 January 2013\|pages=853–860\|doi=10.1145/2463372.2463474\|isbn=9781450319638\|series=Gecco '13\|hdl=1874/290291\|s2cid=9986768}}</ref>		Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many [[estimation of distribution algorithm]]s, for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold.<ref>{{cite book\|last1=Harik\|first1=Georges R.\|last2=Lobo\|first2=Fernando G.\|last3=Sastry\|first3=Kumara\|title=Scalable Optimization via Probabilistic Modeling \|chapter=Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) \|volume=33\|date=1 January 2006\|pages=39–61\|doi=10.1007/978-3-540-34954-9_3\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-34953-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Goldberg\|first2=David E.\|last3=Cantú-Paz\|first3=Erick\|title=BOA: The Bayesian Optimization Algorithm\|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1\|date=1 January 1999\|pages=525–532\|url=http://dl.acm.org/citation.cfm?id=2933973\|isbn=9781558606111\|series=Gecco'99}}</ref> Although good results have been reported for some [[Time complexity#classes of problems]], skepticism concerning the generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms.<ref>{{cite book\|last1=Coffin\|first1=David\|last2=Smith\|first2=Robert E.\|title=Linkage in Evolutionary Computation \|chapter=Linkage Learning in Estimation of Distribution Algorithms \|volume=157\|date=1 January 2008\|pages=141–156\|doi=10.1007/978-3-540-85068-7_7\|language=en\|series=Studies in Computational Intelligence\|isbn=978-3-540-85067-0}}</ref><ref>{{cite journal\|last1=Echegoyen\|first1=Carlos\|last2=Mendiburu\|first2=Alexander\|last3=Santana\|first3=Roberto\|last4=Lozano\|first4=Jose A.\|title=On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms\|journal=Evolutionary Computation\|date=8 November 2012\|volume=21\|issue=3\|pages=471–495\|doi=10.1162/EVCO_a_00095\|pmid=23136917\|s2cid=26585053\|issn=1063-6560}}</ref><ref>{{cite book\|last1=Sadowski\|first1=Krzysztof L.\|last2=Bosman\|first2=Peter A.N.\|last3=Thierens\|first3=Dirk\|title=Proceedings of the 15th annual conference on Genetic and evolutionary computation \|chapter=On the usefulness of linkage processing for solving MAX-SAT \|date=1 January 2013\|pages=853–860\|doi=10.1145/2463372.2463474\|isbn=9781450319638\|series=Gecco '13\|hdl=1874/290291\|s2cid=9986768}}</ref>

	== Limitations ==		== Limitations ==

Geysirhead: removed Category:Evolutionary algorithms using HotCat not needed

2024-12-28T09:40:15Z

removed Category:Evolutionary algorithms using HotCat not needed

← Previous revision		Revision as of 09:40, 28 December 2024
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	{{DEFAULTSORT:Genetic Algorithm}}		{{DEFAULTSORT:Genetic Algorithm}}
	[[Category:Genetic algorithms\| ]]		[[Category:Genetic algorithms\| ]]
	[[Category:Evolutionary algorithms]]
	[[Category:Search algorithms]]		[[Category:Search algorithms]]
	[[Category:Cybernetics]]		[[Category:Cybernetics]]

Geysirhead: removed Category:Digital organisms using HotCat A genetic algorithm is not a self-replicating algorithm, contains replicating population parts, but does not contain self-replicating parts

2024-12-27T17:09:28Z

removed Category:Digital organisms using HotCat A genetic algorithm is not a self-replicating algorithm, contains replicating population parts, but does not contain self-replicating parts

← Previous revision		Revision as of 17:09, 27 December 2024
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	[[Category:Search algorithms]]		[[Category:Search algorithms]]
	[[Category:Cybernetics]]		[[Category:Cybernetics]]
	[[Category:Digital organisms]]

	[[sv:Genetisk programmering#Genetisk algoritm]]		[[sv:Genetisk programmering#Genetisk algoritm]]

Citation bot: Altered url. URLs might have been anonymized. Added isbn. | Use this bot. Report bugs. | Suggested by Jay8g | Linked from User:Jay8g/sandbox | #UCB_webform_linked 127/428

2024-11-03T01:32:28Z

Altered url. URLs might have been anonymized. Added isbn. | Use this bot. Report bugs. | Suggested by Jay8g | Linked from User:Jay8g/sandbox | #UCB_webform_linked 127/428

← Previous revision		Revision as of 01:32, 3 November 2024
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	[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]		[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]
	<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->		<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->
	In [[computer science]] and [[operations research]], a '''genetic algorithm (GA)''' is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA).<ref>{{Cite book \|last1=Pétrowski \|first1=Alain \|last2=Ben-Hamida \|first2=Sana \|title=Evolutionary algorithms \|publisher=John Wiley & Sons \|page=30 \|year=2017 \|url=https://books.google.~~co.uk~~/books?~~hl=en&lr=&~~id=GlGpDgAAQBAJ~~&oi=fnd&pg=PP2~~&dq=genetic+algorithm+evolutionary+algorithms&~~ots~~=~~zk8iTgy5Qc&sig=_7oaR0-lTQLqo-4jJXyCTu88WQ4&redir_esc=y#v=onepage&q=genetic%20algorithm%20evolutionary%20algorithms&f=false~~}}</ref> Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s via biologically inspired operators such as [[selection (genetic algorithm)\|selection]], [[crossover (genetic algorithm)\|crossover]], and [[Mutation (genetic algorithm)\|mutation]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], and [[causal inference]].<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref>		In [[computer science]] and [[operations research]], a '''genetic algorithm (GA)''' is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA).<ref>{{Cite book \|last1=Pétrowski \|first1=Alain \|last2=Ben-Hamida \|first2=Sana \|title=Evolutionary algorithms \|publisher=John Wiley & Sons \|page=30 \|year=2017 \|isbn=978-1-119-13638-5 \|url=https://books.google.com/books?id=GlGpDgAAQBAJ&dq=genetic+algorithm+evolutionary+algorithms&pg=PP2}}</ref> Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s via biologically inspired operators such as [[selection (genetic algorithm)\|selection]], [[crossover (genetic algorithm)\|crossover]], and [[Mutation (genetic algorithm)\|mutation]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], and [[causal inference]].<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref>

	== Methodology ==		== Methodology ==

Perceptron599: Reorder 'selection', 'crossover', 'mutation' to reflect the actual sequence of steps in the algorithm

2024-09-29T11:58:32Z

Reorder 'selection', 'crossover', 'mutation' to reflect the actual sequence of steps in the algorithm

← Previous revision		Revision as of 11:58, 29 September 2024
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	[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]		[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]
	<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->		<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->
	In [[computer science]] and [[operations research]], a '''genetic algorithm~~'''~~ (~~'''~~GA''') is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA).<ref>{{Cite book \|last1=Pétrowski \|first1=Alain \|last2=Ben-Hamida \|first2=Sana \|title=Evolutionary algorithms \|publisher=John Wiley & Sons \|page=30 \|year=2017 \|url=https://books.google.co.uk/books?hl=en&lr=&id=GlGpDgAAQBAJ&oi=fnd&pg=PP2&dq=genetic+algorithm+evolutionary+algorithms&ots=zk8iTgy5Qc&sig=_7oaR0-lTQLqo-4jJXyCTu88WQ4&redir_esc=y#v=onepage&q=genetic%20algorithm%20evolutionary%20algorithms&f=false}}</ref> Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s ~~by relying on~~ biologically inspired operators such as [[~~Mutation~~ (genetic algorithm)\|~~mutation~~]], [[crossover (genetic algorithm)\|crossover]] and [[~~selection~~ (genetic algorithm)\|~~selection~~]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], [[causal inference]],<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref> ~~etc.~~		In [[computer science]] and [[operations research]], a '''genetic algorithm (GA)''' is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA).<ref>{{Cite book \|last1=Pétrowski \|first1=Alain \|last2=Ben-Hamida \|first2=Sana \|title=Evolutionary algorithms \|publisher=John Wiley & Sons \|page=30 \|year=2017 \|url=https://books.google.co.uk/books?hl=en&lr=&id=GlGpDgAAQBAJ&oi=fnd&pg=PP2&dq=genetic+algorithm+evolutionary+algorithms&ots=zk8iTgy5Qc&sig=_7oaR0-lTQLqo-4jJXyCTu88WQ4&redir_esc=y#v=onepage&q=genetic%20algorithm%20evolutionary%20algorithms&f=false}}</ref> Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s via biologically inspired operators such as [[selection (genetic algorithm)\|selection]], [[crossover (genetic algorithm)\|crossover]], and [[Mutation (genetic algorithm)\|mutation]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], and [[causal inference]].<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref>

	== Methodology ==		== Methodology ==

Neutral Editor 645: Added source

2024-09-25T03:07:00Z

Added source

← Previous revision		Revision as of 03:07, 25 September 2024
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	[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]		[[Image:St 5-xband-antenna.jpg\|thumb\|The 2006 NASA [[Space Technology 5\|ST5]] spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an [[evolved antenna]].]]
	<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->		<!-- Deleted image removed: [[Image:ESA JAXA HUMIES Trajectory.png\|thumb\|The ESA/JAXA interplanetary Trajectory recipient of the [http://www.genetic-programming.org/combined.php 2013 gold HUMIES ] award. This complex tour of the Jovian Moons was found with the help of an evolutionary technique based on self-adaptation]] -->
	In [[computer science]] and [[operations research]], a '''genetic algorithm''' ('''GA''') is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA). Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s by relying on biologically inspired operators such as [[Mutation (genetic algorithm)\|mutation]], [[crossover (genetic algorithm)\|crossover]] and [[selection (genetic algorithm)\|selection]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], [[causal inference]],<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref> etc.		In [[computer science]] and [[operations research]], a '''genetic algorithm''' ('''GA''') is a [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA).<ref>{{Cite book \|last1=Pétrowski \|first1=Alain \|last2=Ben-Hamida \|first2=Sana \|title=Evolutionary algorithms \|publisher=John Wiley & Sons \|page=30 \|year=2017 \|url=https://books.google.co.uk/books?hl=en&lr=&id=GlGpDgAAQBAJ&oi=fnd&pg=PP2&dq=genetic+algorithm+evolutionary+algorithms&ots=zk8iTgy5Qc&sig=_7oaR0-lTQLqo-4jJXyCTu88WQ4&redir_esc=y#v=onepage&q=genetic%20algorithm%20evolutionary%20algorithms&f=false}}</ref> Genetic algorithms are commonly used to generate high-quality solutions to [[Optimization (mathematics)\|optimization]] and [[Search algorithm\|search problem]]s by relying on biologically inspired operators such as [[Mutation (genetic algorithm)\|mutation]], [[crossover (genetic algorithm)\|crossover]] and [[selection (genetic algorithm)\|selection]].{{sfn\|Mitchell\|1996\|p=2}} Some examples of GA applications include optimizing [[Decision tree learning\|decision trees]] for better performance, solving [[Sudoku solving algorithms\|sudoku puzzles]],<ref>{{Cite book\|last1=Gerges\|first1=Firas\|last2=Zouein\|first2=Germain\|last3=Azar\|first3=Danielle\|title=Proceedings of the 2018 International Conference on Computing and Artificial Intelligence \|chapter=Genetic Algorithms with Local Optima Handling to Solve Sudoku Puzzles \|date=2018-03-12\|chapter-url=https://doi.org/10.1145/3194452.3194463\|series=ICCAI 2018\|location=New York, NY, USA\|publisher=Association for Computing Machinery\|pages=19–22\|doi=10.1145/3194452.3194463\|isbn=978-1-4503-6419-5\|s2cid=44152535 }}</ref> [[hyperparameter optimization]], [[causal inference]],<ref>{{cite journal \|last1=Burkhart \|first1=Michael C. \|last2=Ruiz \|first2=Gabriel \|title=Neuroevolutionary representations for learning heterogeneous treatment effects \|journal=Journal of Computational Science \|date=2023 \|volume=71 \|page=102054 \|doi=10.1016/j.jocs.2023.102054 \|s2cid=258752823 \|doi-access=free }}</ref> etc.

	== Methodology ==		== Methodology ==