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Genetic representation
| Part of a series on the |
| Evolutionary algorithm |
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| Genetic algorithm (GA) |
| Genetic programming (GP) |
| Differential evolution |
| Evolution strategy |
| Evolutionary programming |
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In computer programming, genetic representation is a way of presenting solutions/individuals in evolutionary computation methods. The term encompasses both the concrete data structures and data types used to realize the genetic material of the candidate solutions in the form of a genome, and the relationships between search space and problem space. In the simplest case, the search space corresponds to the problem space (direct representation).[1] The choice of problem representation is tied to the choice of genetic operators, both of which have a decisive effect on the efficiency of the optimization.[2][3] Genetic representation can encode appearance, behavior, physical qualities of individuals. Difference in genetic representations is one of the major criteria drawing a line between known classes of evolutionary computation.[4][5]
Terminology is often analogous with natural genetics. The block of computer memory that represents one candidate solution is called an individual. The data in that block is called a chromosome. Each chromosome consists of genes. The possible values of a particular gene are called alleles. A programmer may represent all the individuals of a population using binary encoding, permutational encoding, encoding by tree, or any one of several other representations.[6][7]
- ^ Eiben, A.E.; Smith, J.E. (2015). Introduction to Evolutionary Computing. Natural Computing Series. Berlin, Heidelberg: Springer. p. 40. doi:10.1007/978-3-662-44874-8. ISBN 978-3-662-44873-1. S2CID 20912932.
- ^ Rothlauf, Franz (2002). Representations for Genetic and Evolutionary Algorithms. Studies in Fuzziness and Soft Computing. Vol. 104. Heidelberg: Physica-Verlag HD. p. 31. doi:10.1007/978-3-642-88094-0. ISBN 978-3-642-88096-4.
- ^ Eiben, A.E.; Smith, J.E. (2015). "Representation and the Roles of Variation Operators". Introduction to Evolutionary Computing. Natural Computing Series. Berlin, Heidelberg: Springer. pp. 49–51. doi:10.1007/978-3-662-44874-8. ISBN 978-3-662-44873-1. S2CID 20912932.
- ^ Eiben, A.E.; Smith, J.E. (2015). "Popular Evolutionary Algorithm Variants". Introduction to Evolutionary Computing. Natural Computing Series. Berlin, Heidelberg: Springer. pp. 99–118. doi:10.1007/978-3-662-44874-8. ISBN 978-3-662-44873-1. S2CID 20912932.
- ^ Fogel, D.B. (1995). "Phenotypes, genotypes, and operators in evolutionary computation". Proceedings of 1995 IEEE International Conference on Evolutionary Computation. Vol. 1. Perth, WA, Australia: IEEE. pp. 193–198. doi:10.1109/ICEC.1995.489143. ISBN 978-0-7803-2759-7. S2CID 17755853.
- ^ Tomáš Kuthan and Jan Lánský. "Genetic Algorithms in Syllable-Based Text Compression". 2007. p. 26.
- ^ Eiben, A.E.; Smith, J.E. (2015). "Representation, Mutation, and Recombination". Introduction to Evolutionary Computing. Natural Computing Series. Berlin, Heidelberg: Springer. pp. 49–78. doi:10.1007/978-3-662-44874-8. ISBN 978-3-662-44873-1. S2CID 20912932.
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