By the same authors

Solving Real-valued Optimisation Problems using 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
Pages1724-1730
Number of pages7
PublisherASSOC COMPUTING MACHINERY
Place of PublicationNEW YORK
Original languageEnglish
ISBN (Print)978-1-59593-697-4

Abstract

Classical Evolutionary Programming (CEP) and Fast Evolutionary Programming (FEP) have been applied to real-valued function optimisation. Both of these techniques directly evolve the real-values that are the arguments of the real-valued function. lit this paper we have applied a form of genetic programming called Cartesian Genetic Programming (CGP) to a number of real-valued optimisation benchmark problems. The approach we have taken is to evolve a computer program that controls a writing-head, which moves along and interacts with a finite set of symbols that are interpreted as real numbers, instead of manipulating the real numbers directly. In other studies, CGP has already been shown to benefit front a high degree of neutrality. We hope to exploit this for real-valued function optimisation problems to avoid being trapped oil local optima. We have also used an extended form of CGP called Embedded CGP (ECGP) which allows the acquisition, evolution and re-use of modules. The effectiveness of CGP and ECGP are compared and contrasted with CEP and FEP oil the benchmark problems. Results show that the new techniques are very effective.

    Research areas

  • Cartesian Genetic Programming, Real-valued Function Optimisation, Modules, Evolutionary Programming

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