Galvan-Lopez, Edgar and McDermott, James and O'Neill, Michael and Brabazon, Anthony (2010) Defining locality in genetic programming to predict performance. In: Evolutionary Computation (CEC), 2010 IEEE Congress on, August 2010, Barcelona, Spain.

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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.

Item Type:	Conference or Workshop Item (Paper)
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15422
Identification Number:	https://doi.org/10.1109/CEC.2010.5586095
Depositing User:	Edgar Galvan
Date Deposited:	07 Feb 2022 14:46
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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