research-article

Challenging heuristics: evolving binary constraint satisfaction problems

Authors:

Jorge H. Moreno-Scott,

Jose Carlos Ortíz-Bayliss,

Hugo Terashima-Marín,

Santiago Enrique Conant-PablosAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 409 - 416

https://doi.org/10.1145/2330163.2330222

Published: 07 July 2012 Publication History

Abstract

In computer science it is a common practice to evaluate the performance of algorithms using a set of benchmark or randomly generated instances. However, following that approach, the weaknesses of the algorithms may not be exposed. This work is the first phase of research project on coevolution of solutions methods versus problem instances. The goal of study is to generate a method to find difficult to solve problem instances capable of challenging the solution methods or algorithms under analysis, helping to discover opportunities for improvement. An evolutionary model is proposed to find hard binary constraint satisfaction problem instances for different variable ordering heuristics. We characterize the search space by generating random instances with different values for the constraint density and tightness. For all the heuristics, the most difficult problems are located in the same region of the space near to the phase transition. However, there are certain regions of the search space where a heuristic dominates the others, especially where the problems are solvable. Finally, we compare the hardest instances found during the search space exploration with the outcome instances of the evolutionary model. The results show that evolved instances are harder to solve than the ones randomly generated.

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Cited By

Liu STang KYao X(2022)Generative Adversarial Construction of Parallel PortfoliosIEEE Transactions on Cybernetics10.1109/TCYB.2020.298454652:2(784-795)Online publication date: Feb-2022
https://doi.org/10.1109/TCYB.2020.2984546
Akgün ÖDang NMiguel ISalamon AStone C(2019)Instance Generation via Generator InstancesPrinciples and Practice of Constraint Programming10.1007/978-3-030-30048-7_1(3-19)Online publication date: 23-Sep-2019
https://doi.org/10.1007/978-3-030-30048-7_1
Abidin D(2018)A Hybrid Genetic - Differential Evolution Algorithm (HybGADE) for a Constrained Sequencing Problem2018 International Conference on Artificial Intelligence and Data Processing (IDAP)10.1109/IDAP.2018.8620825(1-6)Online publication date: Sep-2018
https://doi.org/10.1109/IDAP.2018.8620825
Show More Cited By

Index Terms

Challenging heuristics: evolving binary constraint satisfaction problems
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2012

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GECCO '12

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GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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

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Cited By

Liu STang KYao X(2022)Generative Adversarial Construction of Parallel PortfoliosIEEE Transactions on Cybernetics10.1109/TCYB.2020.298454652:2(784-795)Online publication date: Feb-2022
https://doi.org/10.1109/TCYB.2020.2984546
Akgün ÖDang NMiguel ISalamon AStone C(2019)Instance Generation via Generator InstancesPrinciples and Practice of Constraint Programming10.1007/978-3-030-30048-7_1(3-19)Online publication date: 23-Sep-2019
https://doi.org/10.1007/978-3-030-30048-7_1
Abidin D(2018)A Hybrid Genetic - Differential Evolution Algorithm (HybGADE) for a Constrained Sequencing Problem2018 International Conference on Artificial Intelligence and Data Processing (IDAP)10.1109/IDAP.2018.8620825(1-6)Online publication date: Sep-2018
https://doi.org/10.1109/IDAP.2018.8620825
Buzdalov MShalyto A(2014)Worst-Case Execution Time Test Generation for Solutions of the Knapsack Problem Using a Genetic AlgorithmBio-Inspired Computing - Theories and Applications10.1007/978-3-662-45049-9_1(1-10)Online publication date: 2014
https://doi.org/10.1007/978-3-662-45049-9_1

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