Editing Evolutionary algorithm (section)

=== No free lunch theorem ===
The [[no free lunch theorem]] of optimization states that all optimization strategies are equally effective when the set of all optimization problems is considered. Under the same condition, no evolutionary algorithm is fundamentally better than another. This can only be the case if the set of all problems is restricted. This is exactly what is inevitably done in practice. Therefore, to improve an EA, it must exploit problem knowledge in some form (e.g. by choosing a certain mutation strength or a [[Genetic representation|problem-adapted coding]]). Thus, if two EAs are compared, this constraint is implied. In addition, an EA can use problem specific knowledge by, for example, not randomly generating the entire start population, but creating some individuals through [[Heuristic (computer science)|heuristics]] or other procedures.<ref name="Davis91">{{Cite book |last=Davis |first=Lawrence |url=https://www.worldcat.org/oclc/23081440 |title=Handbook of genetic algorithms |date=1991 |publisher=Van Nostrand Reinhold |isbn=0-442-00173-8 |location=New York |oclc=23081440}}</ref><ref>{{Citation |last1=Lienig |first1=Jens |title=An evolutionary algorithm for the routing of multi-chip modules |date=1994 |url=http://link.springer.com/10.1007/3-540-58484-6_301 |work=Parallel Problem Solving from Nature — PPSN III |volume=866 |pages=588–597 |editor-last=Davidor |editor-first=Yuval |place=Berlin, Heidelberg |publisher=Springer |doi=10.1007/3-540-58484-6_301 |isbn=978-3-540-58484-1 |access-date=2022-10-18 |last2=Brandt |first2=Holger |editor2-last=Schwefel |editor2-first=Hans-Paul |editor3-last=Männer |editor3-first=Reinhard|url-access=subscription }}</ref> Another possibility to tailor an EA to a given problem domain is to involve suitable heuristics, [[Local search (optimization)|local search procedures]] or other problem-related procedures in the process of generating the offspring. This form of extension of an EA is also known as a [[memetic algorithm]]. Both extensions play a major role in practical applications, as they can speed up the search process and make it more robust.<ref name="Davis91" /><ref>{{Cite book |url=http://link.springer.com/10.1007/978-3-642-23247-3 |title=Handbook of Memetic Algorithms |date=2012 |publisher=Springer Berlin Heidelberg |isbn=978-3-642-23246-6 |editor-last=Neri |editor-first=Ferrante |series=Studies in Computational Intelligence |volume=379 |location=Berlin, Heidelberg |language=en |doi=10.1007/978-3-642-23247-3 |editor-last2=Cotta |editor-first2=Carlos |editor-last3=Moscato |editor-first3=Pablo}}</ref>