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Identification and prediction using symbolic regression alpha-beta: preliminary results

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L. M. Torres-TreviñoAuthors Info & Claims

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1367 - 1372

https://doi.org/10.1145/2598394.2609859

Published: 12 July 2014 Publication History

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Abstract

A novel approach is proposed for generating equations from measured data of dynamic processes. A composition of unary (alpha) and binary (beta) functions is represented by a real vector and adapted by an evolutionary algorithm to build mathematical equations. The equations can be used for identification and prediction considering a mathematical model with specific number of inputs and outputs. Three cases are used for illustration of the approach where mathematical models and plots of theirs performance are presented with promising results.

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Luis M. Torres-Treviño Symbolic Regression Using alpha, beta Operators and Estimation of Distribution Algorithms: Preliminary Results 3rd symbolic regression and modeling workshop for GECCO 2011 July 12-16, 2011, Dublin, Ireland. Distributed on CD-ROM at GECCO-2011.

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Cited By

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González-Campos GTorres-Treviño LLuévano-Hipólito ECruz A(2014)Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression α-βProceedings of the 2014 13th Mexican International Conference on Artificial Intelligence10.1109/MICAI.2014.33(174-179)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/MICAI.2014.33

Index Terms

Identification and prediction using symbolic regression alpha-beta: preliminary results
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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González-Campos GTorres-Treviño LLuévano-Hipólito ECruz A(2014)Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression α-βProceedings of the 2014 13th Mexican International Conference on Artificial Intelligence10.1109/MICAI.2014.33(174-179)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/MICAI.2014.33

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