A Semantics based Symbolic Regression Framework for Mining Explicit and Implicit Equations from Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7650

@InProceedings{Huynh:2016:GECCOcomp,

• author = "Quang Nhat Huynh and Hemant Kumar Singh and Tapabrata Ray",
• title = "A Semantics based Symbolic Regression Framework for Mining Explicit and Implicit Equations from Data",
• booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
• year = "2016",
• editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing",
• pages = "103--104",
• month = "20-24 " # jul,
• keywords = "genetic algorithms, genetic programming: Poster",
• organisation = "SIGEVO",
• address = "Denver, USA",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• isbn13 = "978-1-4503-4323-7",
• DOI = "doi:10.1145/2908961.2908989",
• abstract = "Symbolic Regression (SR) is commonly used to identify relationships among variables and responses in a data in the form of analytical, preferably compact expressions. Genetic Programming (GP) is one of the common ways to perform SR. Such relationships could be represented using explicit or implicit expressions, of which the former has been more extensively studied in literature. Some of the key challenges that face SR are bloat, loss of diversity, and accurate determination of coefficients. More recently, semantics and multi-objective formulations have been suggested as potential tools to build more intelligence in the search process. However, studies along both these directions have been in isolation and applied only to selected components of SR so far. In this paper, we intend to build a framework that integrates semantics deeper into more components of SR. The framework could be operated in traditional single objective as well as multi-objective mode and is capable of dealing with both explicit and implicit functions. The constituent modules use semantics for compaction of expressions, maintaining diversity by identifying unique individuals, crossover and local exploitation. A comparison of obtained results with those from existing semantics-based and multi-objective approach demonstrates the advantages of the proposed framework.",
• notes = "Distributed at GECCO-2016.",

}

Genetic Programming entries for Quang Nhat Huynh Hemant Kumar Singh Tapabrata Ray

Citations