Editing Chromosome (evolutionary algorithm) (section)

{{Short description|Set of parameters for a genetic or evolutionary algorithm}}
{{Evolutionary algorithms}}

A '''chromosome''' or '''genotype''' in [[evolutionary algorithm]]s (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called ''individuals'' according to the biological model, is known as the ''[[Population model (evolutionary algorithm)|population]]''.<ref name=ga-description>{{cite web|title=Introduction to genetic algorithms: IV. Genetic Algorithm|url=http://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php|accessdate=12 August 2015}}</ref><ref name=":0">{{Cite book |last1=Eiben |first1=A.E. |url=https://link.springer.com/10.1007/978-3-662-44874-8 |title=Introduction to Evolutionary Computing |last2=Smith |first2=J.E. |date=2015 |publisher=Springer |isbn=978-3-662-44873-1 |series=Natural Computing Series |location=Berlin, Heidelberg |pages=28–34 |language=en |chapter=Components of Evolutionary Algorithms |doi=10.1007/978-3-662-44874-8|s2cid=20912932 }}</ref> The genome of an individual consists of one, more rarely of several,<ref>{{Citation |last=Baine |first=Nicholas |title=A simple multi-chromosome genetic algorithm optimization of a Proportional-plus-Derivative Fuzzy Logic Controller |date=2008 |url=https://ieeexplore.ieee.org/document/4531273 |work=NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society |pages=1–5 |publisher=IEEE |doi=10.1109/NAFIPS.2008.4531273 |isbn=978-1-4244-2351-4 |s2cid=46591432 |url-access=subscription }}</ref><ref>{{Citation |last1=Peng |first1=Jin |last2=Chu |first2=Zhang Shu |title=A Hybrid Multi-chromosome Genetic Algorithm for the Cutting Stock Problem |date=2010 |url=https://ieeexplore.ieee.org/document/5694457 |work=3rd International Conference on Information Management, Innovation Management and Industrial Engineering |pages=508–511 |publisher=IEEE |doi=10.1109/ICIII.2010.128 |isbn=978-1-4244-8829-2 |s2cid=15608610 |url-access=subscription }}</ref> chromosomes and corresponds to the [[genetic representation]] of the task to be solved. A chromosome is composed of a set of genes, where a gene consists of one or more semantically connected [[Parameter|parameters]], which are often also called ''decision variables''. They determine one or more [[Phenotype|phenotypic]] characteristics of the individual or at least have an influence on them.<ref name=":0" />  In the basic form of genetic algorithms, the chromosome is represented as a binary [[string (computer science)|string]],<ref>{{Cite book |last=Holland |first=John H. |url= |title=Adaptation in natural and artificial systems |date=1992 |publisher=MIT Press |isbn=0-585-03844-9 |edition= |location=Cambridge, Mass. |language=en |oclc=42854623}}</ref> while in later variants<ref>{{Citation |last1=Janikow |first1=C.Z. |title=An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms |date=1991 |url=http://www.cs.umsl.edu/~janikow/publications/1991/GAbin/text.pdf |work=Proceedings of the Fourth International Conference on Genetic Algorithms |pages=31–36 |editor-last=Belew |editor-first=Richard K. |editor2-last=Booker |editor2-first=Lashon B. |place=San Francisco, CA |publisher=Morgan Kaufmann Publishers |isbn=1-55860-208-9 |last2=Michalewicz |first2=Z.}}</ref><ref name=ga-tutorial>{{cite journal |last1=Whitley |first1=Darrell |title=A genetic algorithm tutorial |journal=Statistics and Computing |date=June 1994 |volume=4 |issue=2 |doi=10.1007/BF00175354 |citeseerx=10.1.1.184.3999| s2cid=3447126}}<!--|accessdate=12 August 2015--></ref> and in EAs in general, a wide variety of other [[data structure]]s are used.<ref name=":1">{{Cite journal |last=Whitley |first=Darrell |date=2001 |title=An overview of evolutionary algorithms: practical issues and common pitfalls |url=https://linkinghub.elsevier.com/retrieve/pii/S0950584901001884 |journal=Information and Software Technology |language=en |volume=43 |issue=14 |pages=817–831 |doi=10.1016/S0950-5849(01)00188-4|s2cid=18637958 |url-access=subscription }}</ref><ref>{{Citation |last1=Bäck |first1=Thomas |last2=Hoffmeister |first2=Frank |last3=Schwefel |first3=Hans-Paul |title=A Survey of Evolution Strategies |date=1991 |url=https://www.academia.edu/27025389 |work=Proceedings of the Fourth International Conference on Genetic Algorithms |pages=2–9 |editor-last=Belew |editor-first=Richard K. |editor2-last=Booker |editor2-first=Lashon B. |place=San Francisco, CA |publisher=Morgan Kaufmann Publishers |isbn=1-55860-208-9 }}</ref><ref name=":5">{{Cite book |last=Koza |first=John R. |url=https://www.worldcat.org/oclc/26263956 |title=Genetic programming : on the programming of computers by means of natural selection |date=1992 |publisher=MIT Press |isbn=0-262-11170-5 |location=Cambridge, Mass. |oclc=26263956}}</ref>