Editing Evolutionary algorithm (section)

{{Short description|Subset of evolutionary computation}}
{{Evolutionary algorithms}}
{{Artificial intelligence|Approaches}}
'''Evolutionary algorithms''' ('''EA''') reproduce essential elements of the [[biological evolution]] in a [[computer algorithm]] in order to solve "difficult" problems, at least [[Approximation|approximately]], for which no exact or satisfactory solution methods are known. They belong to the class of [[Metaheuristic|metaheuristics]] and are a [[subset]] of [[Population Based Bio-Inspired Algorithms|population based bio-inspired algorithm]]s<ref>{{cite journal |last1=Farinati |first1=Davide |last2=Vanneschi |first2=Leonardo |title=A survey on dynamic populations in bio-inspired algorithms |journal=Genetic Programming and Evolvable Machines |date=December 2024 |volume=25 |issue=2 |doi=10.1007/s10710-024-09492-4|hdl=10362/170138 |hdl-access=free }}</ref> and [[evolutionary computation]], which itself are part of the field of [[computational intelligence]].<ref name="EVOALG">{{cite book |last=Vikhar |first=P. A. |title=2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC) |year=2016 |isbn=978-1-5090-0467-6 |location=Jalgaon |pages=261–265 |chapter=Evolutionary algorithms: A critical review and its future prospects |doi=10.1109/ICGTSPICC.2016.7955308 |s2cid=22100336}}</ref> The mechanisms of biological evolution that an EA mainly imitates are [[reproduction]], [[mutation]], [[genetic recombination|recombination]] and [[natural selection|selection]]. [[Candidate solution]]s to the [[optimization problem]] play the role of individuals in a population, and the [[fitness function]] determines the quality of the solutions (see also [[loss function]]). [[Evolution]] of the population then takes place after the repeated application of the above operators.

Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying [[fitness landscape]]. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of [[microevolution|microevolutionary processes]] and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor.<ref name="VLSI">{{cite book |last1=Cohoon |first1=J. P. |url=https://www.ifte.de/mitarbeiter/lienig/cohoon.pdf |title="Evolutionary Algorithms for the Physical Design of VLSI Circuits" in Advances in Evolutionary Computing: Theory and Applications |last2=Karro |first2=J. |last3=Lienig |first3=J. |publisher=Springer Verlag |year=2003 |isbn=978-3-540-43330-9 |location=London |pages=683–712}}</ref> In fact, this computational complexity is due to fitness function evaluation. [[Fitness approximation]] is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems;<ref name=":0">{{Cite journal |last1=Slowik |first1=Adam |last2=Kwasnicka |first2=Halina |date=2020 |title=Evolutionary algorithms and their applications to engineering problems |journal=Neural Computing and Applications |language=en |volume=32 |issue=16 |pages=12363–12379 |doi=10.1007/s00521-020-04832-8 |s2cid=212732659 |issn=0941-0643|doi-access=free }}</ref><ref name=":1">{{Cite journal |last1=Mika |first1=Marek |last2=Waligóra |first2=Grzegorz |last3=Węglarz |first3=Jan |date=2011 |title=Modelling and solving grid resource allocation problem with network resources for workflow applications |url=http://link.springer.com/10.1007/s10951-009-0158-0 |journal=Journal of Scheduling |language=en |volume=14 |issue=3 |pages=291–306 |doi=10.1007/s10951-009-0158-0 |s2cid=31859338 |issn=1094-6136|url-access=subscription }}</ref><ref name=":2">{{Cite web |url=https://www.evostar.org/ |title=International Conference on the Applications of Evolutionary Computation |author=<!--Not stated--> |publisher=The conference is part of the Evo* series. The conference proceedings are published by Springer |access-date=2022-12-23 }}</ref> therefore, there may be no direct link between algorithm complexity and problem complexity.