Evolutionary programming: Difference between revisions

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{{Short description|Evolutionary algorithm with a defined structure}}
{{Short description|Evolutionary algorithm with a defined structure}}
{{Evolutionary algorithms}}
{{Evolutionary algorithms}}
'''Evolutionary programming''' is an [[evolutionary algorithm]], where a share of new population is created by mutation of previous population without [[Crossover (evolutionary algorithm)|crossover]].<ref name=overview>{{cite journal |last1=Slowik |first1=Adam |last2=Kwasnicka |first2=Halina |title=Evolutionary algorithms and their applications to engineering problems |journal=Neural Computing and Applications |date=1 August 2020 |volume=32 |issue=16 |pages=12363–12379 |doi=10.1007/s00521-020-04832-8 |language=en |issn=1433-3058|doi-access=free }}</ref><ref>{{cite journal |last1=Abido |first1=Mohammad A. |last2=Elazouni |first2=Ashraf |title=Modified multi-objective evolutionary programming algorithm for solving project scheduling problems |journal=Expert Systems with Applications |date=30 November 2021 |volume=183 |pages=115338 |doi=10.1016/j.eswa.2021.115338 |url=https://www.sciencedirect.com/science/article/abs/pii/S0957417421007673 |issn=0957-4174|url-access=subscription }}</ref> Evolutionary programming differs from [[evolution strategy]] ES(<math>\mu+\lambda</math>) in one detail.<ref name=overview/> All individuals are selected for the new population, while in ES(<math>\mu+\lambda</math>), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm [[programming paradigms|paradigms]].<ref>{{cite journal |last1=Brameier |first1=Markus |title=On Linear Genetic Programming |journal=Dissertation |date=2004 |url=http://d-nb.info:80/1011533146/34 |access-date=27 December 2024}}</ref>
'''Evolutionary programming''' is an [[evolutionary algorithm]], where a share of new population is created by mutation of previous population without [[Crossover (evolutionary algorithm)|crossover]].<ref name=overview>{{cite journal |last1=Slowik |first1=Adam |last2=Kwasnicka |first2=Halina |title=Evolutionary algorithms and their applications to engineering problems |journal=Neural Computing and Applications |date=1 August 2020 |volume=32 |issue=16 |pages=12363–12379 |doi=10.1007/s00521-020-04832-8 |language=en |issn=1433-3058|doi-access=free }}</ref><ref>{{cite journal |last1=Abido |first1=Mohammad A. |last2=Elazouni |first2=Ashraf |title=Modified multi-objective evolutionary programming algorithm for solving project scheduling problems |journal=Expert Systems with Applications |date=30 November 2021 |volume=183 |article-number=115338 |doi=10.1016/j.eswa.2021.115338 |url=https://www.sciencedirect.com/science/article/abs/pii/S0957417421007673 |issn=0957-4174|url-access=subscription }}</ref> Evolutionary programming differs from [[evolution strategy]] ES(<math>\mu+\lambda</math>) in one detail.<ref name=overview/> All individuals are selected for the new population, while in ES(<math>\mu+\lambda</math>), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm [[programming paradigms|paradigms]].<ref>{{cite journal |last1=Brameier |first1=Markus |title=On Linear Genetic Programming |journal=Dissertation |date=2004 |url=http://d-nb.info:80/1011533146/34 |access-date=27 December 2024}}</ref>


==History==
==History==

Latest revision as of 05:06, 30 September 2025

Template:Short description Template:Evolutionary algorithms Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover.[1][2] Evolutionary programming differs from evolution strategy ES(μ+λ) in one detail.[1] All individuals are selected for the new population, while in ES(μ+λ), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms.[3]

History

It was first used by Lawrence J. Fogel in the US in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence.[4] It was used to evolve finite-state machines as predictors.[5]

Timeline of EP - selected algorithms[1]
Year Description Reference
1966 EP introduced by Fogel et al. [6]
1992 Improved fast EP - Cauchy mutation is used instead of Gaussian mutation [7]
2002 Generalized EP - usage of Lévy-type mutation [8]
2012 Diversity-guided EP - Mutation step size is guided by diversity [9]
2013 Adaptive EP - The number of successful mutations determines the strategy parameter [10]
2014 Social EP - Social cognitive model is applied meaning replacing individuals with cognitive agents [11]
2015 Immunised EP - Artificial immune system inspired mutation and selection [12]
2016 Mixed mutation strategy EP - Gaussian, Cauchy and Lévy mutations are used [13]
2017 Fast Convergence EP - An algorithm, which boosts convergence speed and solution quality [14]
2017 Immune log-normal EP - log-normal mutation combined with artificial immune system [15]
2018 ADM-EP - automatically designed mutation operators [16]

See also

References

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External links

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