Genetic programming: the ratio of crossover to mutation as a function of time

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dc.contributor.authorMunroe, David R.
dc.date.accessioned2013-05-14T02:31:34Z
dc.date.available2013-05-14T02:31:34Z
dc.date.issued2004
dc.description.abstractThis article studies the sub-tree operators: mutation and crossover, within the context of Genetic Programming. Two standard problems, symbolic linear regression and a non-linear tree, were presented to the algorithm at each stage. The behaviour of the operators in regard to fitness is first established, followed by an analysis of the most optimal ratio between crossover and mutation. Subsequently, three algorithms are presented as candidates to dynamically learn the most optimal level of this ratio. The results of each algorithm are then compared to each other and the traditional constant ratio.en
dc.identifier.citationMunroe, D.R. (2004), Genetic programming: the ratio of crossover to mutation as a function of time, Research Letters in the Information and Mathematical Sciences, 6, 83-96en
dc.identifier.issn1175-2777
dc.identifier.urihttp://hdl.handle.net/10179/4429
dc.language.isoenen
dc.publisherMassey Universityen
dc.subjectGenetic programmingen
dc.subjectGenetic mutationen
dc.titleGenetic programming: the ratio of crossover to mutation as a function of timeen
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