On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems

Edgar Galván-López 1 Lucia Vázquez-Mendoza 2 Marc Schoenauer 3, 4 Leonardo Trujillo 5
3 TAU - TAckling the Underspeficied
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : In Genetic Programming (GP), the fitness of individuals is normally computed by using a set of fitness cases (FCs). Research on the use of FCs in GP has primarily focused on how to reduce the size of these sets. However, often, only a small set of FCs is available and there is no need to reduce it. In this work, we are interested in using the whole FCs set, but rather than adopting the commonly used GP approach of presenting the entire set of FCs to the system from the beginning of the search, referred as static FCs, we allow the GP system to build it by aggregation over time, named as dynamic FCs, with the hope to make the search more amenable. Moreover, there is no study on the use of FCs in Dynamic Optimisation Problems (DOPs). To this end, we also use the Kendall Tau Distance (KTD) approach, which quantifies pairwise dissimilarities among two lists of fitness values. KTD aims to capture the degree of a change in DOPs and we use this to promote structural diversity. Results on eight symbolic regression functions indicate that both approaches are highly beneficial in GP.
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Communication dans un congrès
EA 2017- International Conference on Artificial Evolution, Oct 2017, Paris, France. pp.1-14, 2017, 〈https://ea2017.inria.fr/〉
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Dernière modification le : jeudi 5 avril 2018 - 12:30:26

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Edgar Galván-López, Lucia Vázquez-Mendoza, Marc Schoenauer, Leonardo Trujillo. On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems. EA 2017- International Conference on Artificial Evolution, Oct 2017, Paris, France. pp.1-14, 2017, 〈https://ea2017.inria.fr/〉. 〈hal-01648365〉

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