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


    Galvan, Edgar and Vazquez, Lucia and Schoenauer, Marc and Trujillo, Leonardo (2018) On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems. In: EA 2017: Artificial Evolution. Lecture Notes in Computer Science book series (LNCS 10764) . Springer, pp. 72-87. ISBN 9783319781327

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    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.

    Item Type: Book Section
    Additional Information: Cite as: Galván-López E., Vázquez-Mendoza L., Schoenauer M., Trujillo L. (2018) On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems. In: Lutton E., Legrand P., Parrend P., Monmarché N., Schoenauer M. (eds) Artificial Evolution. EA 2017. Lecture Notes in Computer Science, vol 10764. Springer, Cham
    Keywords: Genetic programming; Fitness Cases; Static and Dynamic Optimisation Problems;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 10275
    Identification Number: https://doi.org/10.1007/978-3-319-78133-4_6
    Depositing User: Edgar Galvan
    Date Deposited: 04 Dec 2018 16:40
    Publisher: Springer
    Refereed: Yes
    URI:

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