Editing Evolutionary computation (section)

== Techniques ==
Evolutionary computing techniques mostly involve [[metaheuristic]] [[Mathematical optimization|optimization]] [[algorithm]]s. Broadly speaking, the field includes:
*[[Agent-based modeling]]
**[[Ant colony optimization]]
**[[Particle swarm optimization]]
**[[Swarm intelligence]]
*[[Artificial immune system]]s
*[[Artificial life]] 
**[[Digital organism]]
*[[Cultural algorithm]]s
*[[Differential evolution]]
*[[Dual-phase evolution]]
*[[Estimation of distribution algorithm]]
*[[Evolutionary algorithm]]
**[[Genetic algorithm]]
**[[Evolutionary programming]]
**[[Genetic programming]]
***[[Gene expression programming]]
***[[Grammatical evolution]]
**[[Evolution strategy]]
*[[Learnable evolution model]]
*[[Learning classifier system]]
*[[Memetic algorithms]]
*[[Neuroevolution]]
*[[Self-organization]] such as [[self-organizing map]]s, [[competitive learning]]
A thorough catalogue with many other recently proposed algorithms has been published in the [https://github.com/fcampelo/EC-Bestiary Evolutionary Computation Bestiary].<ref>{{Cite journal |last1=Campelo |first1=Felipe |last2=Aranha |first2=Claus |date=2018-06-20 |title=Ec Bestiary: A Bestiary Of Evolutionary, Swarm And Other Metaphor-Based Algorithms |url=https://zenodo.org/record/1293035 |language=en |doi=10.5281/ZENODO.1293035}}</ref> It is important to note that many recent algorithms, however, have poor experimental validation.<ref>{{Cite journal |last=Kudela |first=Jakub |date=2022-12-12 |title=A critical problem in benchmarking and analysis of evolutionary computation methods |url=https://www.nature.com/articles/s42256-022-00579-0 |journal=Nature Machine Intelligence |language=en |volume=4 |issue=12 |pages=1238–1245 |arxiv=2301.01984 |doi=10.1038/s42256-022-00579-0 |s2cid=254616518 |issn=2522-5839}}</ref>