NDLI: Crossover and mutation operators for grammar-guided genetic programming

Content Provider	Springer Nature Link
Author	Couchet, Jorge Manrique, Daniel Ríos, Juan Rodríguez Patón, Alfonso
Copyright Year	2006
Abstract	This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.
Starting Page	943
Ending Page	955
Page Count	13
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	11
Issue Number	10
e-ISSN	14337479
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-12-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Grammar-guided genetic programming Crossover Mutation Breast cancer prognosis Control Engineering Mathematical Logic and Foundations Computing Methodologies Theory of Computation Numerical and Computational Methods in Engineering
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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