Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@InProceedings{MousaviAstarabadi:2015:GECCOcomp,

• author = "Samaneh Sadat {Mousavi Astarabadi} and Mohammad Mehdi Ebadzadeh",
• title = "Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of {GP} Trees",
• booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr",
• pages = "1441--1442",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Multi Objective Optimization, Symbolic Regression, Derivative, Generalization: Poster",
• isbn13 = "978-1-4503-3488-4",
• URL = "http://doi.acm.org/10.1145/2739482.2764662",
• DOI = "doi:10.1145/2739482.2764662",
• size = "2 pages",
• abstract = "Genetic programming (GP) is widely used for constructing models with applications in control, classification, regression, etc.; however, it has some shortcomings, such as generalization. This paper proposes to enhance the GP generalization by controlling the first order derivative of GP trees in the evolution process. To achieve this goal, a multi-objective GP is implemented. Then, the first order derivative of GP trees is considered as one of its objectives. The proposed method is evaluated on several benchmark problems to provide an experimental validation. The experiments demonstrate the usefulness of the proposed method with the capability of achieving compact solutions with reasonable accuracy on training data and better accuracy on test data.",
• notes = "Also known as \cite{2764662} Distributed at GECCO-2015.",

}

Genetic Programming entries for Samaneh Sadat Mousavi Astarabadi Mohammad Mehdi Ebadzadeh

Citations