Examining the landscape of semantic similarity based mutation

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Title: Examining the landscape of semantic similarity based mutation
Authors: Nguyen, Quang Uy
Nguyen, Xuan Hoai
O'Neill, Michael
Permanent link: http://hdl.handle.net/10197/3514
Date: 12-Jul-2011
Abstract: This paper examines how the semantic locality of a search operator affects the fitness landscape of Genetic Programming (GP). We compare the fitness landscapes of GP search when standard subtree mutation and a recently proposed semantic-based mutation, Semantic Similarity-based Mutation (SSM), are used. The comparison is based on two well-studied fitness landscape measures, namely, the autocorrelation function and information content. The experiments were conducted on a family of symbolic regression problems with increasing degrees of difficulty. The results show that SSM helps to significantly smooth out the fitness landscape of GP compared to standard subtree mutation. This gives an explanation for the better performance of SSM over standard subtree mutation operator.
Funding Details: Irish Research Council for Science, Engineering and Technology
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2011 ACM
Keywords: SemanticsGenetic programmingFitness landscapeMutation
Subject LCSH: Genetic programming (Computer science)
Semantic computing
Evolutionary computation
DOI: 10.1145/2001576.2001760
Language: en
Status of Item: Peer reviewed
Is part of: GECCO '11 : Proceedings of the 13th annual conference on Genetic and evolutionary computation
Conference Details: Paper presented at the ACM Genetic and Evolutionary Computation Conference, GECCO 2011, 12-16 July, Dublin, Ireland
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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