Approximating geometric crossover in semantic space

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Approximating geometric crossover in semantic space
We propose a crossover operator that works with genetic programming trees and is approximately geometric crossover in the semantic space. By defining semantic as program’s evaluation profile with respect to a set of fitness cases and constraining to a specific class of metric-based fitness functions, we cause the fitness landscape in the semantic space to have perfect fitness-distance correlation. The proposed approximately geometric semantic crossover exploits this property of the semantic fitness landscape by an appropriate sampling. We demonstrate also how the proposed method may be conveniently combined with hill climbing. We discuss the properties of the methods, and describe an extensive computational experiment concerning logical function synthesis and symbolic regression. Categories and Subject Descriptors: I.2.8 [Problem Solving, Control Methods, and Search]: Heuristic methods General Terms: Algorithms
Krzysztof Krawiec, Pawel Lichocki
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Authors Krzysztof Krawiec, Pawel Lichocki
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