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Geometric Semantic Genetic Programming

Moraglio, Alberto and Krawiec, Krzysztof and Johnson, Colin G. (2012) Geometric Semantic Genetic Programming. In: Parallel Problem Solving from Nature - PPSN XII 12th International Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 21-31. ISBN 978-3-642-32936-4. E-ISBN 978-3-642-32937-1. (doi:10.1007/978-3-642-32937-1_3)

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Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view on search operators and representations, we bring the semantic approach to its extreme consequences and introduce a novel form of GP – Geometric Semantic GP (GSGP) – that searches directly the space of the underlying semantics of the programs. This perspective provides new insights on the relation between program syntax and semantics, search operators and fitness landscape, and allows for principled formal design of semantic search operators for different classes of problems. We de- rive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-642-32937-1_3
Uncontrolled keywords: Genetic Programming, Boolean Function, Output Vector, Semantic Operator, Search Operator
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Colin Johnson
Date Deposited: 18 Oct 2018 16:53 UTC
Last Modified: 29 May 2019 09:20 UTC
Resource URI: (The current URI for this page, for reference purposes)
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