Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty

Book Chapters

  1. Edgar Galvan, Leonardo Trujillo, James McDermott and Ahmed Kattan. Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty. In Oliver Schütze, Carlos A Coello Coello, Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bouvry, Pierre Del Moral and Pierrick Legrand (eds.). EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing series, volume 175, Springer Berlin Heidelberg, 2013, pages 41-56. URL, DOI BibTeX

    @incollection{,
    	year = 2013,
    	isbn = "978-3-642-31518-3",
    	booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II",
    	volume = 175,
    	series = "Advances in Intelligent Systems and Computing",
    	editor = "Schütze, Oliver and Coello Coello, Carlos A. and Tantar, Alexandru-Adrian and Tantar, Emilia and Bouvry, Pascal and Del Moral, Pierre and Legrand, Pierrick",
    	doi = "10.1007/978-3-642-31519-0_3",
    	title = "Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty",
    	url = "http://dx.doi.org/10.1007/978-3-642-31519-0_3",
    	publisher = "Springer Berlin Heidelberg",
    	author = "Galvan, Edgar and Trujillo, Leonardo and McDermott, James and Kattan, Ahmed",
    	pages = "41-56"
    }
    

Abstract

It is commonly accepted that a mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. Locality has been classified in one of two categories: high and low locality. It is said that a representation has high locality if most genotypic neighbours correspond to phenotypic neighbours. The opposite is true for a representation that has low locality. It is argued that a representation with high locality performs better in evolutionary search compared to a representation that has low locality. In this work, we explore, for the first time, a study on Genetic Programming (GP) locality in continuous fitnessvalued cases. For this, we extended the original definition of locality (first defined and used in Genetic Algorithms using bitstrings) from genotype-phenotype mapping to the genotype-fitness mapping. Then, we defined three possible variants of locality in GP regarding neighbourhood. The experimental tests presented here use a set of symbolic regression problems, two different encoding and two different mutation operators. We show how locality can be studied in this type of scenarios (continuous fitness-valued cases) and that locality can successfully been used as a performance prediction tool.

Published in
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II
Advances in Intelligent Systems and Computing
Pages 41-56
Volume 175
http://link.springer.com/chapter/10.1007%2F978-3-642-31519-0_3
Copyright
2012
ISSN
2194-5357
ISBN
978-3-642-31519-0
Last modified onSaturday, 12 October 2013 17:29
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