Geometric Semantic Genetic Programming (GSGP) is a recently introduced framework to design domain-specific search operators for Genetic Programming (GP) to search directly the semantic space of functions. The fitness landscape seen by GSGP is always - for any domain and for any problem - unimodal with a constant slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. We design and analyse a mutation-based GSGP for the class of all classification tree learning problems, which is a classic GP application domain.

Theory-laden design of mutation-based Geometric Semantic Genetic Programming for learning classification trees

Manzoni Luca;
2013-01-01

Abstract

Geometric Semantic Genetic Programming (GSGP) is a recently introduced framework to design domain-specific search operators for Genetic Programming (GP) to search directly the semantic space of functions. The fitness landscape seen by GSGP is always - for any domain and for any problem - unimodal with a constant slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. We design and analyse a mutation-based GSGP for the class of all classification tree learning problems, which is a classic GP application domain.
2013
978-1-4799-0454-9
978-1-4799-0453-2
978-1-4799-0451-8
978-1-4799-0452-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2947952
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