Introducing Semantic-Clustering Selection in Grammatical Evolution

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Title: Introducing Semantic-Clustering Selection in Grammatical Evolution
Authors: Forstenlechner, StefanNicolau, MiguelFagan, DavidO'Neill, Michael
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Date: 15-Jul-2015
Online since: 2016-01-07T10:27:05Z
Abstract: Semantics has gained much attention in the last few years and new advanced crossover and mutation operations have been created which use semantic information to improve the quality and generalisability of individuals in genetic programming. In this paper we present a new selection operator in grammatical evolution which uses semantic information of individuals instead of just the fitness value. The semantic traits of an individual are stored in a vector. An unsupervised learning technique is used to cluster individuals based on their semantic vector. Individuals are only allowed to reproduce with individuals from the same cluster to preserve semantic locality and intensify the search in a certain semantic area. At the same time, multiple semantic areas are covered by the search as there exist multiple clusters which cover different areas and therefore preserve semantic diversity. This new selection operator is tested on several symbolic regression benchmark problems and compared to grammatical evolution with tournament selection to analyse its performance.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Start page: 1277
End page: 1284
Copyright (published version): 2015 the Authors
Keywords: Grammatical evolutionSemanticSelection
DOI: 10.1145/2739482.2768502
Language: en
Status of Item: Peer reviewed
Is part of: GECCO 2015 Companion: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
Conference Details: 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO 2015), Madrid, Spain, July, 2015
Appears in Collections:Business Research Collection
CASL Research Collection

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