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.


Publié dans:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 987-994
Présenté à:
11th Annual conference on Genetic and evolutionary computation (GECCO-2009), Montreal, Québec, Canada, July 8-12, 2009
Année
2009
Publisher:
New York, NY, USA, Association for Computing Machinery
ISBN:
978-1-60558-325-9
Mots-clefs:
Laboratoires:




 Notice créée le 2010-03-26, modifiée le 2019-03-16

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