Editing Evolutionary algorithm (section)

==Comparison to other concepts==
===Biological processes===

A possible limitation{{According to whom|date=May 2013}} of many evolutionary algorithms is their lack of a clear [[genotype–phenotype distinction]]. In nature, the fertilized egg cell undergoes a complex process known as [[embryogenesis]] to become a mature [[phenotype]]. This indirect [[encoding]] is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the [[evolvability]] of the organism.<ref>G.S. Hornby and J.B. Pollack. "Creating high-level components with a generative representation for body-brain evolution". ''[[Artificial Life (journal)|Artificial Life]]'', 8(3):223–246, 2002.</ref><ref>Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. [http://www.ofria.com/pubs/2009CluneEtAl.pdf "Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding"] {{Webarchive|url=https://web.archive.org/web/20160603205252/http://www.ofria.com/pubs/2009CluneEtAl.pdf |date=2016-06-03 }}. ''Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics'', 2009. Trondheim, Norway.</ref> Such indirect (also known as generative or developmental) encodings also enable evolution to exploit the regularity in the environment.<ref>J. Clune, C. Ofria, and R. T. Pennock, [http://jeffclune.com/publications/Clune-HyperNEATandRegularity.pdf "How a generative encoding fares as problem-regularity decreases"], in ''PPSN'' (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of ''Lecture Notes in Computer Science'', pp. 358–367, Springer, 2008.</ref> Recent work in the field of [[artificial development|artificial embryogeny]], or artificial developmental systems, seeks to address these concerns. And [[gene expression programming]] successfully explores a genotype–phenotype system, where the genotype consists of linear multigenic chromosomes of fixed length and the phenotype consists of multiple expression trees or computer programs of different sizes and shapes.<ref>Ferreira, C., 2001. [http://www.gene-expression-programming.com/webpapers/GEP.pdf "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems"]. ''Complex Systems'', Vol. 13, issue 2: 87–129.</ref>{{Synthesis inline|date=May 2013}}

===Monte-Carlo methods===
Both method classes have in common that their individual search steps are determined by chance. The main difference, however, is that EAs, like many other metaheuristics, learn from past search steps and incorporate this experience into the execution of the next search steps in a method-specific form. With EAs, this is done firstly through the fitness-based selection operators for partner choice and the formation of the next generation. And secondly, in the type of search steps: In EA, they start from a current solution and change it or they mix the information of two solutions. In contrast, when dicing out new solutions in [[Monte Carlo method|Monte-Carlo methods]], there is usually no connection to existing solutions.<ref>{{Cite book |last=Schwefel |first=Hans-Paul |url=https://www.researchgate.net/publication/220690578 |title=Evolution and Optimum Seeking |date=1995 |publisher=Wiley |isbn=978-0-471-57148-3 |series=Sixth-generation computer technology series |location=New York |pages=109}}</ref><ref>{{Cite book |url=https://www.worldcat.org/title/ocm44807816 |title=Evolutionary Computation 1 |date=2000 |publisher=Institute of Physics Publishing |isbn=978-0-7503-0664-5 |editor-last=Fogel |editor-first=David B. |location=Bristol ; Philadelphia |pages=xxx and xxxvii (Glossary) |oclc=ocm44807816 |editor-last2=Bäck |editor-first2=Thomas |editor-last3=Michalewicz |editor-first3=Zbigniew}}</ref>

If, on the other hand, the search space of a task is such that there is nothing to learn, Monte-Carlo methods are an appropriate tool, as they do not contain any algorithmic overhead that attempts to draw suitable conclusions from the previous search. An example of such tasks is the proverbial ''search for a needle in a haystack'', e.g. in the form of a flat (hyper)plane with a single narrow peak.