Login
Login

Evolutionary programming

{{Short description|Evolutionary algorithm with a defined structure}}

{{Evolutionary algorithms}}

Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover.{{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 }}{{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 }} Evolutionary programming differs from evolution strategy ES(\mu+\lambda) in one detail. All individuals are selected for the new population, while in ES(\mu+\lambda), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms.{{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}}

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.{{cite book |date=2009 |doi=10.1109/9780470544600.ch7|isbn=978-0-470-54460-0 |chapter=Artificial Intelligence through Simulated Evolution |title=Evolutionary Computation }} It was used to evolve finite-state machines as predictors.{{cite journal |last1=Abraham |first1=Ajith |last2=Nedjah |first2=Nadia |last3=Mourelle |first3=Luiza de Macedo |title=Evolutionary Computation: from Genetic Algorithms to Genetic Programming |journal=Genetic Systems Programming: Theory and Experiences |series=Studies in Computational Intelligence |date=2006 |volume=13 |pages=1–20 |doi=10.1007/3-540-32498-4_1 |url=https://link.springer.com/chapter/10.1007/3-540-32498-4_1 |publisher=Springer |isbn=978-3-540-29849-6 |language=en|url-access=subscription }}

class="wikitable sortable"

|+ Timeline of EP - selected algorithms

YearDescriptionReference
1966EP introduced by Fogel et al.{{cite book |last1=Fogel |first1=LJ |last2=Owens |first2=AJ |last3=Walsh |first3=MJ |title=rtificial intelligence thorough simulated evolution |date=1966 |publisher=Wiley |location=New York}}
1992Improved fast EP - Cauchy mutation is used instead of Gaussian mutation{{cite journal |title=Evolutionary programming made faster |journal=IEEE Transactions on Evolutionary Computation |date=July 1999 |volume=3 |issue=2 |pages=82–102 |doi=10.1109/4235.771163 |last1=Xin Yao |last2=Yong Liu |last3=Guangming Lin }}
2002Generalized EP - usage of Lévy-type mutation{{cite journal |last1=Iwamatsu |first1=Masao |title=Generalized evolutionary programming with Lévy-type mutation |journal=Computer Physics Communications |date=1 August 2002 |volume=147 |issue=1 |pages=729–732 |doi=10.1016/S0010-4655(02)00386-7 |bibcode=2002CoPhC.147..729I |url=https://www.sciencedirect.com/science/article/abs/pii/S0010465502003867 |issn=0010-4655|url-access=subscription }}
2012Diversity-guided EP - Mutation step size is guided by diversity{{cite journal |last1=Alam |first1=Mohammad Shafiul |last2=Islam |first2=Md. Monirul |last3=Yao |first3=Xin |last4=Murase |first4=Kazuyuki |title=Diversity Guided Evolutionary Programming: A novel approach for continuous optimization |journal=Applied Soft Computing |date=1 June 2012 |volume=12 |issue=6 |pages=1693–1707 |doi=10.1016/j.asoc.2012.02.002 |url=https://www.sciencedirect.com/science/article/abs/pii/S1568494612000567 |issn=1568-4946|url-access=subscription }}
2013Adaptive EP - The number of successful mutations determines the strategy parameter{{cite journal |last1=Das |first1=Swagatam |last2=Mallipeddi |first2=Rammohan |last3=Maity |first3=Dipankar |title=Adaptive evolutionary programming with p-best mutation strategy |journal=Swarm and Evolutionary Computation |date=1 April 2013 |volume=9 |pages=58–68 |doi=10.1016/j.swevo.2012.11.002 |url=https://www.sciencedirect.com/science/article/abs/pii/S221065021200079X |issn=2210-6502|url-access=subscription }}
2014Social EP - Social cognitive model is applied meaning replacing individuals with cognitive agents{{cite journal |last1=Nan |first1=LI |last2=Xiaomin |first2=BAI |last3=Shouzhen |first3=ZHU |last4=Jinghong |first4=ZHENG |title=Social Evolutionary Programming Algorithm onUnit Commitment in Wind Power Integrated System |journal=IFAC Proceedings Volumes |date=1 January 2014 |volume=47 |issue=3 |pages=3611–3616 |doi=10.3182/20140824-6-ZA-1003.00384 |url=https://www.sciencedirect.com/science/article/pii/S1474667016421658 |issn=1474-6670|url-access=subscription }}
2015Immunised EP - Artificial immune system inspired mutation and selection{{cite journal |last1=Gao |first1=Wei |title=Slope stability analysis based on immunised evolutionary programming |journal=Environmental Earth Sciences |date=1 August 2015 |volume=74 |issue=4 |pages=3357–3369 |doi=10.1007/s12665-015-4372-0 |bibcode=2015EES....74.3357G |url=https://link.springer.com/article/10.1007/s12665-015-4372-0 |language=en |issn=1866-6299|url-access=subscription }}
2016Mixed mutation strategy EP - Gaussian, Cauchy and Lévy mutations are used{{cite book |last1=Pang |first1=Jinwei |last2=Dong |first2=Hongbin |last3=He |first3=Jun |last4=Feng |first4=Qi |title=2016 IEEE Congress on Evolutionary Computation (CEC) |chapter=Mixed mutation strategy evolutionary programming based on Shapley value |date=July 2016 |pages=2805–2812 |doi=10.1109/CEC.2016.7744143|isbn=978-1-5090-0623-6 }}
2017Fast Convergence EP - An algorithm, which boosts convergence speed and solution quality{{cite journal |last1=Basu |first1=Mousumi |title=Fast Convergence Evolutionary Programming for Multi-area Economic Dispatch |journal=Electric Power Components and Systems |date=14 September 2017 |volume=45 |issue=15 |pages=1629–1637 |doi=10.1080/15325008.2017.1376234 |issn=1532-5008}}
2017Immune log-normal EP - log-normal mutation combined with artificial immune system{{cite book |last1=Mansor |first1=M.H. |last2=Musirin |first2=I. |last3=Othman |first3=M.M. |chapter=Immune Log-Normal Evolutionary Programming (ILNEP) for solving economic dispatch problem with prohibited operating zones |title=2017 4th International Conference on Industrial Engineering and Applications (ICIEA) |date=April 2017 |pages=163–167 |doi=10.1109/IEA.2017.7939199|isbn=978-1-5090-6774-9 }}
2018ADM-EP - automatically designed mutation operators{{cite journal |last1=Hong |first1=Libin |last2=Drake |first2=John H. |last3=Woodward |first3=John R. |last4=Özcan |first4=Ender |title=A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming |journal=Applied Soft Computing |date=1 January 2018 |volume=62 |pages=162–175 |doi=10.1016/j.asoc.2017.10.002 |url=https://www.sciencedirect.com/science/article/abs/pii/S1568494617306051 |issn=1568-4946}}

See also

References

External links

  • [https://web.archive.org/web/20120225015603/http://www.aip.de/~ast/EvolCompFAQ/Q1_2.htm The Hitch-Hiker's Guide to Evolutionary Computation: What's Evolutionary Programming (EP)?]
  • [http://www.cleveralgorithms.com/nature-inspired/evolution/evolutionary_programming.html Evolutionary Programming by Jason Brownlee (PhD)] {{Webarchive|url=https://web.archive.org/web/20130118233236/http://www.cleveralgorithms.com/nature-inspired/evolution/evolutionary_programming.html |date=2013-01-18 }}

{{Evolutionary computation}}

{{Authority control}}

{{DEFAULTSORT:Evolutionary Programming}}

Category:Evolutionary algorithms

de:Evolutionäre Programmierung

{{compu-sci-stub}}