Editing Gene expression programming (section)

==Background==
[[Evolutionary algorithms]] use populations of individuals, select individuals according to fitness, and introduce genetic variation using one or more [[genetic operators]]. Their use in artificial computational systems dates back to the 1950s where they were used to solve optimization problems (e.g. Box 1957<ref>Box, G. E. P., 1957. [https://www.gwern.net/docs/statistics/decision/1957-box.pdf Evolutionary operation: A method for increasing industrial productivity]. Applied Statistics, 6, 81–101.</ref> and Friedman 1959<ref>Friedman, G. J., 1959. Digital simulation of an evolutionary process. General Systems Yearbook, 4, 171–184.</ref>). But it was with the introduction of [[evolution strategies]] by Rechenberg in 1965<ref>{{cite book|last=Rechenberg|first=Ingo|year=1973|title=Evolutionsstrategie|place=Stuttgart|publisher=Holzmann-Froboog|isbn=3-7728-0373-3}}</ref> that evolutionary algorithms gained popularity. A good overview text on evolutionary algorithms is the book "An Introduction to Genetic Algorithms" by Mitchell (1996).<ref>{{cite book|last= Mitchell|first= Melanie|year=1996|title='An Introduction to Genetic Algorithms|url= https://archive.org/details/introductiontoge00mitc|url-access= registration|place= Cambridge, MA|publisher= MIT Press|isbn= 978-0-262-13316-6}}</ref>

Gene expression programming<ref>{{cite web|last=Ferreira|first=C.|year=2001|title=Gene Expression Programming: A New Adaptive Algorithm for Solving Problems|url= http://www.gene-expression-programming.com/webpapers/GEP.pdf|publisher= Complex Systems, Vol. 13, issue 2: 87–129}}</ref> belongs to the family of [[evolutionary algorithm]]s and is closely related to [[genetic algorithms]] and [[genetic programming]]. From genetic algorithms it inherited the linear chromosomes of fixed length; and from genetic programming it inherited the expressive [[parse tree]]s of varied sizes and shapes.

In gene expression programming the linear chromosomes work as the genotype and the parse trees as the phenotype, creating a [[Genotype-phenotype distinction|genotype/phenotype system]]. This genotype/phenotype system is [[Gene|multigenic]], thus encoding multiple parse trees in each chromosome. This means that the computer programs created by GEP are composed of multiple parse trees. Because these parse trees are the result of gene expression, in GEP they are called [[expression tree]]s. Masood Nekoei, et al. utilized this expression programming style in ABC optimization to conduct ABCEP as a method that outperformed other evolutionary algorithms.[https://www.sciencedirect.com/science/article/pii/S0020025520309300?casa_token=ybS_CHnEmEEAAAAA:doOERQ2M96-E7IbQZ540CkxmeP-fOkDq1L0S_bAwfsLyVTgX_SAwPxxhN-27TWgBwGqztCyPyQ ABCEP]