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)

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

Fingerprint Dive into the research topics of 'Novel Genetic Algorithm Crossover Approaches for Time-Series Problems'. Together they form a unique fingerprint.

  • Cite this

    Godley, P. M., Cowie, J., & Cairns, D. E. (2007). Novel Genetic Algorithm Crossover Approaches for Time-Series Problems. In E. Ridge, T. Stützle, M. Birattari, & H. H. Hoos (Eds.), SLS-DS 2007 Doctoral Symposium on Engineering Stochastic Local Search Algorithms (pp. 47-51). (IRIDIA – Technical Report Series).. http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2007-014.pdf