In evolutionary algorithms (EA), the term of premature convergence means that a population for an optimization problem converged too early, resulting in being suboptimal. In this context, the parental solutions, through the aid of genetic operators, are not able to generate offspring that are superior to, or outperform, their parents. Premature convergence is a common problem found in evolutionary algorithms in general and genetic algorithms in particular, as it leads to a loss, or convergence of, a large number of alleles, subsequently making it very difficult to search for a specific gene in which the alleles were present.[1][2] An allele is considered lost if, in a population, a gene is present, where all individuals are sharing the same value for that particular gene. An allele is, as defined by De Jong, considered to be a converged allele, when 95% of a population share the same value for a certain gene (see also convergence).[3]
- ^ Leung, Yee; Gao, Yong; Xu, Zong-Ben (1997). "Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis". IEEE Transactions on Neural Networks. 8 (5): 1165–1176. doi:10.1109/72.623217. ISSN 1045-9227. PMID 18255718.
- ^ Baker, James E. (1985), Grefenstette, John J. (ed.), "Adaptive Selection Methods for Genetic Algorithms", Proceedings of the First International Conference on Genetic Algorithms and their Applications, Hillsdale, NJ: L. Erlbaum, pp. 101–111, ISBN 9780805804263
- ^ De Jong, Kenneth A. (1975). An analysis of the behavior of a class of genetic adaptive systems (PhD). Ann Arbor, MI: University of Michigan. hdl:2027.42/4507.