Redundancies in linear GP, canonical transformation, and its exploitation: A demonstration on image feature synthesis

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dc.contributor.authorWatchareeruetai, Ukrit
dc.contributor.authorTakeuchi, Yoshinori
dc.contributor.authorMatsumoto, Tetsuya
dc.contributor.authorKudo, Hiroaki
dc.contributor.authorOhnishi, Noboru
dc.date.accessioned2026-08-06T10:02:01Z
dc.date.available2026-08-06T10:02:01Z
dc.date.issued2011-03-01
dc.description.abstractThis paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP. © 2010 Springer Science+Business Media, LLC.
dc.identifier.citationGenetic Programming and Evolvable Machines, 12(1), 49-77, 2011
dc.identifier.doi10.1007/s10710-010-9118-x
dc.identifier.issn13892576
dc.identifier.other2-s2.0-79551602750
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/3549
dc.sourceGenetic Programming and Evolvable Machines
dc.subjectCanonical form
dc.subjectCanonical transformation
dc.subjectFeature extraction
dc.subjectLinear genetic programming
dc.subjectRedundant representation
dc.titleRedundancies in linear GP, canonical transformation, and its exploitation: A demonstration on image feature synthesis
dc.typeArticle

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