Introducing the swingometer crossover and mutation operators for floating-point encoded genetic algorithms

Shane Lee*, Hefin Rowlands

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    Genetic algorithms that utilise floating point-encoded chromosomes instead of the traditional binary or Gary code have become wide spread in recent years. This paper introduces new floatingpoint crossover and mutation operators for such genetic algorithms. The operators are derived from examining the implicit constraints that traditional binary crossover and mutation operators impose on the values of the parameters being affected. It is shown by an example that a genetic algorithm using these operators does converge and they should be considered for general use in floating-point genetic algorithms.

    Original languageEnglish
    Title of host publicationSimulation in Wider Europe - 19th European Conference on Modelling and Simulation, ECMS 2005
    Pages103-108
    Number of pages6
    Publication statusPublished - 1 Dec 2005
    Event19th European Conference on Modelling and Simulation, ECMS 2005 - Riga, Latvia
    Duration: 1 Jun 20054 Jun 2005

    Publication series

    NameSimulation in Wider Europe - 19th European Conference on Modelling and Simulation, ECMS 2005

    Conference

    Conference19th European Conference on Modelling and Simulation, ECMS 2005
    Country/TerritoryLatvia
    CityRiga
    Period1/06/054/06/05

    Keywords

    • Floating-point encoded
    • Genetic algorithm
    • Real encoded

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