Editing Evolutionary programming (section)

{{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 (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>