Editing Evolutionary algorithm (section)

==Types==
Similar techniques differ in [[genetic representation]] and other implementation details, and the nature of the particular applied problem.
* [[Genetic algorithm]] – This is the most popular type of EA. One seeks the solution of a problem in the form of strings of numbers (traditionally binary, although the best representations are usually those that reflect something about the problem being solved),<ref name=VLSI/> by applying operators such as recombination and mutation (sometimes one, sometimes both).  This type of EA is often used in [[Optimization (mathematics)|optimization]] problems.
* [[Genetic programming]] – Here the solutions are in the form of computer programs, and their fitness is determined by their ability to solve a computational problem. There are many variants of Genetic Programming:
** [[Cartesian genetic programming]]
** [[Gene expression programming]]
** [[Grammatical evolution]]
** [[Linear genetic programming]]
** [[Multi expression programming]]
* [[Evolutionary programming]] – Similar to evolution strategy, but with a deterministic selection of all parents.
* [[Evolution strategy]] (ES) – Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates. The method is mainly used for numerical optimization, although there are also variants for combinatorial tasks.<ref>{{Citation |last1=Nissen |first1=Volker |last2=Krause |first2=Matthias |title=Fuzzy Logik |chapter=Constrained Combinatorial Optimization with an Evolution Strategy |series=Informatik aktuell |date=1994 |editor-last=Reusch |editor-first=Bernd |url=https://link.springer.com/chapter/10.1007/978-3-642-79386-8_5 |language=en |location=Berlin, Heidelberg |publisher=Springer |pages=33–40 |doi=10.1007/978-3-642-79386-8_5 |isbn=978-3-642-79386-8|url-access=subscription }}</ref><ref>{{Cite journal |last1=Coelho |first1=V. N. |last2=Coelho |first2=I. M. |last3=Souza |first3=M. J. F. |last4=Oliveira |first4=T. A. |last5=Cota |first5=L. P. |last6=Haddad |first6=M. N. |last7=Mladenovic |first7=N. |last8=Silva |first8=R. C. P. |last9=Guimarães |first9=F. G. |year=2016 |title=Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems. |journal=Evol Comput |volume=24 |issue=4 |pages=637–666 |doi=10.1162/EVCO_a_00187|pmid=27258842 |s2cid=13582781 }}</ref><ref name="eaoverview">{{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>
** [[CMA-ES]]
** [[Natural evolution strategy]]
* [[Differential evolution]] – Based on vector differences and is therefore primarily suited for [[numerical optimization]] problems.
* Coevolutionary algorithm – Similar to genetic algorithms and evolution strategies, but the created solutions are compared on the basis of their outcomes from interactions with other solutions. Solutions can either compete or cooperate during the search process. Coevolutionary algorithms are often used in scenarios where the fitness landscape is dynamic, complex, or involves competitive interactions.<ref>{{Citation|last1=Ma |first1=Xiaoliang |last2=Li |first2=Xiaodong |last3=Zhang |first3=Qingfu |last4=Tang |first4=Ke |last5=Liang |first5=Zhengping |last6=Xie |first6=Weixin |last7=Zhu |first7=Zexuan |title=A Survey on Cooperative Co-Evolutionary Algorithms. |date=2019 |url=https://ieeexplore.ieee.org/document/8454482|journal=IEEE Transactions on Evolutionary Computation|volume=23 |number=3|pages=421–441|doi=10.1109/TEVC.2018.2868770|s2cid=125149900 |access-date=2023-05-22|url-access=subscription }}</ref><ref>{{Cite book |chapter-url=https://link.springer.com/referenceworkentry/10.1007/978-3-540-92910-9_31 |title=Handbook of Natural Computing |date=2012 |publisher=Springer Berlin Heidelberg |pages=987–1033
|chapter=Coevolutionary Principles|isbn=978-3-540-92910-9 |editor-last1=Rozenberg |editor-first1=Grzegorz |editor-last2=Bäck |editor-first2=Thomas |editor-last3=Kok |editor-first3=Joost N.|author-first1=Elena |author-last1=Popovici |author-first2=Anthony|author-last2=Bucci |author-first3=R. Paul|author-last3=Wiegand|author-first4=Edwin D.|author-last4=De Jong |location=Berlin, Heidelberg |language=en |doi=10.1007/978-3-540-92910-9_31}}</ref>
* [[Neuroevolution]] – Similar to genetic programming but the genomes represent artificial neural networks by describing structure and connection weights. The genome encoding can be direct or indirect.
* [[Learning classifier system]] – Here the solution is a set of classifiers (rules or conditions). A Michigan-LCS evolves at the level of individual classifiers whereas a Pittsburgh-LCS uses populations of classifier-sets. Initially, classifiers were only binary, but now include real, neural net, or [[S-expression]] types. Fitness is typically determined with either a strength or accuracy based [[reinforcement learning]] or [[supervised learning]] approach.
* Quality–Diversity algorithms – QD algorithms simultaneously aim for high-quality and diverse solutions. Unlike traditional optimization algorithms that solely focus on finding the best solution to a problem, QD algorithms explore a wide variety of solutions across a problem space and keep those that are not just high performing, but also diverse and unique.<ref>{{Cite journal |last1=Pugh |first1=Justin K. |last2=Soros |first2=Lisa B. |last3=Stanley |first3=Kenneth O. |date=2016-07-12 |title=Quality Diversity: A New Frontier for Evolutionary Computation |journal=Frontiers in Robotics and AI |volume=3 |doi=10.3389/frobt.2016.00040 |issn=2296-9144 |doi-access=free }}</ref><ref>{{Cite book |last1=Lehman |first1=Joel |last2=Stanley |first2=Kenneth O. |title=Proceedings of the 13th annual conference on Genetic and evolutionary computation |chapter=Evolving a diversity of virtual creatures through novelty search and local competition |date=2011-07-12 |pages=211–218 |chapter-url=http://dx.doi.org/10.1145/2001576.2001606 |location=New York, NY, USA |publisher=ACM |doi=10.1145/2001576.2001606|isbn=9781450305570 |s2cid=17338175 }}</ref><ref>{{Cite journal |last1=Cully |first1=Antoine |last2=Clune |first2=Jeff |last3=Tarapore |first3=Danesh |last4=Mouret |first4=Jean-Baptiste |date=2015-05-27 |title=Robots that can adapt like animals |url=http://dx.doi.org/10.1038/nature14422 |journal=Nature |volume=521 |issue=7553 |pages=503–507 |doi=10.1038/nature14422 |pmid=26017452 |arxiv=1407.3501 |bibcode=2015Natur.521..503C |s2cid=3467239 |issn=0028-0836}}</ref>