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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Geometric Semantic Genetic Programming
verfasst von : Alberto Moraglio, Krzysztof Krawiec, Colin G. Johnson
Erschienen in: Parallel Problem Solving from Nature - PPSN XII
Verlag: Springer Berlin Heidelberg
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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 derive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators.