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 IEEE.
Mambrini, A., Manzoni, L., Moraglio, A. (2013). Theory-laden design of mutation-based Geometric Semantic Genetic Programming for learning classification trees. In 2013 IEEE Congress on Evolutionary Computation, CEC 2013 (pp.416-423) [10.1109/CEC.2013.6557599].
Theory-laden design of mutation-based Geometric Semantic Genetic Programming for learning classification trees
MANZONI, LUCA;
2013
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 IEEE.
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