Geometric Semantic Genetic Programming

Alberto Moraglio¹, Krzysztof Krawiec², and Colin G. Johnson³

¹School of Computer Science, University of Birmingham, UK
A.Moraglio@cs.bham.ac.uk

²Institute of Computing Science, Poznan University of Technology, Poland
kkrawiec@cs.put.poznan.pl

³School of Computing, University of Kent, UK
C.G.Johnson@kent.ac.uk

Abstract. 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 derive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators.

LNCS 7491, p. 21 ff.

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