Editing Evolutionary algorithm (section)

===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}}