Efficient indexing of similarity models with inequality symbolic regression

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

@InProceedings{Bartovs:2013:GECCO,

• author = "Tomas Bartos and Tomas Skopal and Juraj Mosko",
• title = "Efficient indexing of similarity models with inequality symbolic regression",
• 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 = "901--908",
• keywords = "genetic algorithms, genetic programming",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2463372.2463487",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "The increasing amount of available unstructured content introduced a new concept of searching for information, the content-based retrieval. The principle behind is that the objects are compared based on their content which is far more complex than simple text or metadata based searching. Many indexing techniques arose to provide an efficient and effective similarity searching. However, these methods are restricted to a specific domain such as the metric space model. If this prerequisite is not fulfilled, indexing cannot be used, while each similarity search query degrades to sequential scanning which is unacceptable for large datasets. Inspired by previous successful results, we decided to apply the principles of genetic programming to the area of database indexing. We developed the GP-SIMDEX which is a universal framework that is capable of finding precise and efficient indexing methods for similarity searching for any given similarity data. For this purpose, we introduce the inequality symbolic regression principle and show how it helps the GP-SIMDEX Framework to find appropriate results that in most cases outperform the best-known indexing methods.",
• notes = "Also known as \cite{2463487} 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 Tomas Bartos Tomas Skopal Juraj Mosko

Citations