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A neuro-evolutionary approach to produce general hyper-heuristics for the dynamic variable ordering in hard binary constraint satisfaction problems

Authors:

José Carlos Ortiz-Bayliss,

Hugo Terashima-Marin,

Peter Ross,

Jorge Iván Fuentes-Rosado,

Manuel Valenzuela-RendónAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 1811 - 1812

https://doi.org/10.1145/1569901.1570174

Published: 08 July 2009 Publication History

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Abstract

This paper introduces a neuro-evolutionary approach to produce hyper-heuristics for the dynamic variable ordering for hard binary constraint satisfaction problems. The model uses a GA to evolve a population of neural networks architectures and parameters. For every cycle in the GA process, the new networks are trained using backpropagation. When the process is over, the best trained individual in the last population of neural networks represents the general hyper-heuristic.

References

[1]

R. Dechter and I. Meiri. Experimental evaluation of preprocessing algorithms for constraint satisfaction problems. Artificial Intelligence, 38(2):211--242, 1994.

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[2]

P. Prosser. Binary constraint satisfaction problems: Some are harder than others. In Proceedings of the European Conference in Artificial Intelligence, pages 95--99, Amsterdam, Holland, 1994.

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[3]

S. Russell and P. Norvig. Artificial Intelligence A Modern Approach. Prentice Hall, 1995.

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[4]

H. Terashima-Marín, J. C. Ortiz-Bayliss, P. Ross and M. Valenzuela-Rendón. Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems. Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 571--578. Atlanta, Georgia, USA, 2008

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Moreno-Scott JOrtiz-Bayliss JTerashima-Marín HConant-Pablos S(2016)Experimental matching of instances to heuristics for constraint satisfaction problemsComputational Intelligence and Neuroscience10.1155/2016/73490702016(37-37)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1155/2016/7349070
Ortiz-Bayliss JTerashima-Marin HOzcan EParkes AConant-Pablos S(2013)Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557975(3307-3314)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557975
Ortiz-Bayliss JOzcan EParkes ATerashima-Marin H(2010)Mapping the performance of heuristics for Constraint SatisfactionIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5585965(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5585965

Index Terms

A neuro-evolutionary approach to produce general hyper-heuristics for the dynamic variable ordering in hard binary constraint satisfaction problems
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Information

Published In

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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

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Moreno-Scott JOrtiz-Bayliss JTerashima-Marín HConant-Pablos S(2016)Experimental matching of instances to heuristics for constraint satisfaction problemsComputational Intelligence and Neuroscience10.1155/2016/73490702016(37-37)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1155/2016/7349070
Ortiz-Bayliss JTerashima-Marin HOzcan EParkes AConant-Pablos S(2013)Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557975(3307-3314)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557975
Ortiz-Bayliss JOzcan EParkes ATerashima-Marin H(2010)Mapping the performance of heuristics for Constraint SatisfactionIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5585965(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5585965

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