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On the Use of Semantics in Multi-objective Genetic Programming

Edgar Galván-López 1, 2 Efrén Mezura-Montes 3 Ouassim Ait Elhara 2, 4 Marc Schoenauer 2, 4
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Research on semantics in Genetic Programming (GP) has increased dramatically over the last number of years. Results in this area clearly indicate that its use in GP can considerably increase GP performance. Motivated by these results, this paper investigates for the first time the use of Semantics in Muti-Objective GP, within the well-known NSGA-II algorithm. To this end, we propose two forms of incorporating semantics into a MOGP system. Results on challenging (highly) unbalanced binary classification tasks indicate that the adoption of semantics in MOGP is beneficial, in particular when a semantic distance is incorporated into the core of NSGA-II.
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Submitted on : Tuesday, October 25, 2016 - 7:00:15 PM
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Edgar Galván-López, Efrén Mezura-Montes, Ouassim Ait Elhara, Marc Schoenauer. On the Use of Semantics in Multi-objective Genetic Programming. 14th International Conference Parallel Problem Solving from Nature – PPSN XIV, Sep 2016, Edinburgh, United Kingdom. pp.353 - 363, ⟨10.1007/978-3-319-45823-6_33⟩. ⟨hal-01387632⟩



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