- author = "Edgar Galvan-Lopez and Lucia Vazquez-Mendoza and Marc Schoenauer and Leonardo Trujillo",
- title = "On the Use of Dynamic {GP} Fitness Cases in Static and Dynamic Optimisation Problems",
- booktitle = "EA 2017, International Conference on Artificial Evolution",
- year = "2017",
- editor = "Evelyne Lutton and Pierrick Legrand and Pierre Parrend and Nicolas Monmarche and Marc Schoenauer",
- volume = "10764)",
- series = "LNCS",
- pages = "72--87",
- address = "Paris, France",
- month = oct # " 2017",
- publisher = "Springer",
- note = "Revised Selected Papers",
- keywords = "genetic algorithms, genetic programming",
- bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
- contributor = "TAckling the Underspeficied and Laboratoire de Recherche en Informatique and Evelyne Lutton",
- identifier = "hal-01648365",
- language = "en",
- oai = "oai:HAL:hal-01648365v1",
- isbn13 = "978-3-319-78133-4",
- ISSN = "01648365",
-
URL = "
https://hal.inria.fr/hal-01648365",
-
URL = "https://hal.inria.fr/hal-01648365/document",
-
URL = "
https://hal.inria.fr/hal-01648365/file/dynamic_v3.pdf",
-
DOI = "
10.1007/978-3-319-78133-4_6",
- size = "16 pages",
- 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.",
-
notes = "Evolution Artificielle https://ea2017.inria.fr/
Also known as \cite{oai:HAL:hal-01648365v1}",
