Defining locality in genetic programming to predict performance

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Title: Defining locality in genetic programming to predict performance
Authors: Galván-López, Edgar
McDermott, James
O'Neill, Michael
Brabazon, Anthony
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Date: Jul-2010
Online since: 2010-11-18T16:37:10Z
Abstract: A key indicator of problem difficulty in evolutionary computation problems is the landscape’s locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotype- fitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2010 IEEE
Keywords: Genetic programmingLocalityProblem difficultyEvolutionary computation
Subject LCSH: Genetic programming (Computer science)
Evolutionary computation
Genetic algorithms
DOI: 10.1109/CEC.2010.5586095
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Language: en
Status of Item: Peer reviewed
Is part of: 2010 IEEE Congress on Evolutionary Computation (CEC) [proceedings]
Conference Details: Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July
ISBN: 978-1-4244-6909-3
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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