By the same authors

A New Crossover Technique for Cartesian Genetic Programming Genetic Programming Track

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationGECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2
DatePublished - 2007
Pages1580-1587
Number of pages8
PublisherASSOC COMPUTING MACHINERY
Place of PublicationNEW YORK
Original languageEnglish
ISBN (Print)978-1-59593-697-4

Abstract

Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not; use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested oil two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.

    Research areas

  • Cartesian Genetic Programming, optimization, crossover techniques, NEUTRALITY, LANDSCAPE

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