About the Dynamics of Essential Genetic Information: An Empirical Analysis for Selected GA-Variants

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Abstract

This paper exemplarily points out how essential genetic information evolves during the runs of selected GA-variants. The algorithmic enhancements to a standard genetic algorithm certify the survival of essential genetic information by supporting the survival of relevant alleles rather than the survival of above average chromosomes. This is achieved by defining the survival probability of a new child chromosome depending on the child's fitness in comparison to the fitness values of its own parents. The main aim of this paper is to explain important properties of the discussed algorithm variants in a rather intuitive way. Aspects for meaningful and practically more relevant generalizations as well as more sophisticated experimental analyses are indicated.

Original languageEnglish
Title of host publication2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09
PublisherACM Sigevo
Pages787-790
Number of pages4
ISBN (Print)9781605583266
DOIs
Publication statusPublished - 2009
Event2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit) - Shanghai, China
Duration: 12 Jun 200914 Jun 2009
http://www.sigevo.org/gec-summit-2009/

Publication series

Name2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09

Conference

Conference2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit)
CountryChina
CityShanghai
Period12.06.200914.06.2009
Internet address

Keywords

  • Genetic algorithms
  • Population diversity analysis
  • Premature convergence
  • Selection
  • Self-adaptivity

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