Novel Genetic Algorithm Crossover Approaches for Time-Series Problems

Paul M. Godley, Julie Cowie, David E. Cairns

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Genetic Algorithms (GAs) are a commonly used stochastic search
heuristic which have been applied to a plethora of problem domains.
GAs work on a population of chromosomes (an encoding of a solution
to the problem at hand) and breed solutions from fit parents to
hopefully produce fitter children through a process of crossover and
mutation. This work discusses two novel crossover approaches for GAs
when applied to the optimisation of time-series problems, with particular
application to bio-control schedules.
Original languageEnglish
Title of host publicationSLS-DS 2007 Doctoral Symposium on Engineering Stochastic Local Search Algorithms
EditorsEnda Ridge, Thomas Stützle, Mauro Birattari, Holger H. Hoos
Place of PublicationBelgium
Pages47-51
Number of pages5
Publication statusPublished - 2007

Publication series

NameIRIDIA – Technical Report Series
PublisherIRIDIA
ISSN (Print)1781-3794

Keywords

  • genetic algorithims
  • chromosomes
  • crossover approaches
  • bio-control schedules

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