DocumentCode :
2326888
Title :
Defining locality in genetic programming to predict performance
Author :
Galván-López, Edgar ; McDermott, James ; O´Neill, Michael ; Brabazon, Anthony
Author_Institution :
Natural Comput. Res. & Applic. Group, Univ. Coll. Dublin, Dublin, Ireland
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
8
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.
Keywords :
genetic algorithms; mathematical programming; evolutionary computation problem; genetic programming; genotype-fitness mapping; genotype-phenotype mapping; performance prediction; Clouds; Context; Correlation; Distortion measurement; Encoding; Equations; Genetic programming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2010 IEEE Congress on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6909-3
Type :
conf
DOI :
10.1109/CEC.2010.5586095
Filename :
5586095
Link To Document :
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