Searching for novel clustering programs

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Naredo:2013:GECCO,

• author = "Enrique Naredo and Leonardo Trujillo",
• title = "Searching for novel clustering programs",
• booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference",
• year = "2013",
• editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi",
• isbn13 = "978-1-4503-1963-8",
• pages = "1093--1100",
• keywords = "genetic algorithms, genetic programming",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2463372.2463505",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Novelty search (NS) is an open-ended evolutionary algorithm that eliminates the need for an explicit objective function. Instead, NS focuses selective pressure on the search for novel solutions. NS has produced intriguing results in specialised domains, but has not been applied in most machine learning areas. The key component of NS is that each individual is described by the behaviour it exhibits, and this description is used to determine how novel each individual is with respect to what the search has produced thus far. However, describing individuals in behavioural space is not trivial, and care must be taken to properly define a descriptor for a particular domain. This paper applies NS to a mainstream pattern analysis area: data clustering. To do so, a descriptor of clustering performance is proposed and tested on several problems, and compared with two control methods, Fuzzy C-means and K-means. Results show that NS can effectively be applied to data clustering in some circumstances. NS performance is quite poor on simple or easy problems, achieving basically random performance. Conversely, as the problems get harder NS performs better, and outperforming the control methods. It seems that the search space exploration induced by NS is fully exploited only when generating good solutions is more challenging.",
• notes = "Also known as \cite{2463505} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)",

}

Genetic Programming entries for Enrique Naredo Leonardo Trujillo

Citations