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A chromosome or genotype in evolutionary algorithms (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.[1][2] The genome of an individual consists of one, more rarely of several,[3][4] 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 parameters, which are often also called decision variables. They determine one or more phenotypic characteristics of the individual or at least have an influence on them.[2] In the basic form of genetic algorithms, the chromosome is represented as a binary string,[5] while in later variants[6][7] and in EAs in general, a wide variety of other data structures are used.[8][9][10]
- ^ "Introduction to genetic algorithms: IV. Genetic Algorithm". Retrieved 12 August 2015.
- ^ a b Eiben, A.E.; Smith, J.E. (2015). "Components of Evolutionary Algorithms". Introduction to Evolutionary Computing. Natural Computing Series. Berlin, Heidelberg: Springer. pp. 28–34. doi:10.1007/978-3-662-44874-8. ISBN 978-3-662-44873-1. S2CID 20912932.
- ^ Baine, Nicholas (2008), "A simple multi-chromosome genetic algorithm optimization of a Proportional-plus-Derivative Fuzzy Logic Controller", NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, IEEE, pp. 1–5, doi:10.1109/NAFIPS.2008.4531273, ISBN 978-1-4244-2351-4, S2CID 46591432
- ^ Peng, Jin; Chu, Zhang Shu (2010), "A Hybrid Multi-chromosome Genetic Algorithm for the Cutting Stock Problem", 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, IEEE, pp. 508–511, doi:10.1109/ICIII.2010.128, ISBN 978-1-4244-8829-2, S2CID 15608610
- ^ Holland, John H. (1992). Adaptation in natural and artificial systems. Cambridge, Mass.: MIT Press. ISBN 0-585-03844-9. OCLC 42854623.
- ^ Janikow, C.Z.; Michalewicz, Z. (1991), Belew, Richard K.; Booker, Lashon B. (eds.), "An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms" (PDF), Proceedings of the Fourth International Conference on Genetic Algorithms, San Francisco, CA: Morgan Kaufmann Publishers, pp. 31–36, ISBN 1-55860-208-9
- ^ Whitley, Darrell (June 1994). "A genetic algorithm tutorial". Statistics and Computing. 4 (2). CiteSeerX 10.1.1.184.3999. doi:10.1007/BF00175354. S2CID 3447126.
- ^ Whitley, Darrell (2001). "An overview of evolutionary algorithms: practical issues and common pitfalls". Information and Software Technology. 43 (14): 817–831. doi:10.1016/S0950-5849(01)00188-4. S2CID 18637958.
- ^ Bäck, Thomas; Hoffmeister, Frank; Schwefel, Hans-Paul (1991), Belew, Richard K.; Booker, Lashon B. (eds.), "A Survey of Evolution Strategies", Proceedings of the Fourth International Conference on Genetic Algorithms, San Francisco, CA: Morgan Kaufmann Publishers, pp. 2–9, ISBN 1-55860-208-9
- ^ Koza, John R. (1992). Genetic programming : on the programming of computers by means of natural selection. Cambridge, Mass.: MIT Press. ISBN 0-262-11170-5. OCLC 26263956.