Defining locality in genetic programming to predict performance
|Title:||Defining locality in genetic programming to predict performance||Authors:||Galván-López, Edgar
|Permanent link:||http://hdl.handle.net/10197/2559||Date:||Jul-2010||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 programming;Locality;Problem difficulty;Evolutionary computation||Subject LCSH:||Genetic programming (Computer science)
|DOI:||10.1109/CEC.2010.5586095||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|
|Appears in Collections:||Computer Science Research Collection|
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