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Defining locality in genetic programming to predict performance
Date Issued
2010-07
Date Available
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2010 IEEE
Subject – LCSH
Genetic programming (Computer science)
Evolutionary computation
Genetic algorithms
Web versions
Language
English
Status of Item
Peer reviewed
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
This item is made available under a Creative Commons License
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