Show simple item record

dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2017-03-23T10:37:53Z
dc.date.available2017-03-23T10:37:53Z
dc.date.issued2002
dc.identifier.citationYang, S. (2002) Statistics-based adaptive non-uniform crossover for genetic algorithms. In: J. A. Bullinaria (editor), Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI'02), pp. 201-208en
dc.identifier.urihttp://hdl.handle.net/2086/13826
dc.description.abstractThrough the population, genetic algorithm (GA) implicitly maintains the statistics about the search space. This implicit statistics can be used explicitly to enhance GA's performance. Inspired by this idea, a statistics-based adaptive non-uniform crossover, called SANUX, has been proposed. SANUX uses the statistics information of the alleles in each locus to adaptively calculate the swapping probability of that locus for crossover. A simple triangular function has been used to calculate the swapping probability. In this paper two different functions, the trapezoid and exponential functions, are investigated for SANUX instead of the triangular function. The experiment results show that both functions further improve the performance of SANUX across a typical set of GA's test problems.en
dc.language.isoenen
dc.publisherUniversity of Birmingham, UKen
dc.subjectStatistics-based adaptive non-uniform crossoveren
dc.subjectGenetic algorithmsen
dc.titleStatistics-based adaptive non-uniform crossover for genetic algorithmsen
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record