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A parallel evolutionary algorithm for unconstrained binary quadratic problems

Published: 12 July 2008 Publication History

Abstract

In this paper an island model is described for the unconstrained Binary Quadratic Problem (BQP), which can be used with up to 2500 binary variables. Our island model uses a master-slave structure and the migration is centralized. In the model a basic evolutionary algorithm (EA) runs which is a hybrid, steady-state EA. The basic EA uses a new mutation operator that is composed of two parts and based on a modified version of an explicit collective memory method (EC-memory), the Virtual Loser [2].We tested our island model on the benchmark problems from the OR-Library. Comparing the results with other heuristic methods, we can conclude that our algorithm is highly effective in solving large instances of the BQP; it has a high probability of finding the best-known solutions.

References

[1]
Merz, P. and Katayama, K. A Hybrid Evolutionary Local Search Approach for the Unconstrained Binary Quadratic Programming Problem. Bio Systems. Vol. 78. No. 1--3. pp 99--118.
[2]
Sebag, M., Schoenauer, M. and Ravisé, C. Toward Civilized Evolution: Developing Inhibitions. In: Bäck T (ed): Proc. of the 7th International Conference on Genetic Algorithm. Morgan Kaufmann Pub. San Francisco, 1997. pp 291--298

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  • (2017)A Parallel Tabu Search for the Unconstrained Binary Quadratic Programming problem2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969360(557-564)Online publication date: 5-Jun-2017

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  1. A parallel evolutionary algorithm for unconstrained binary quadratic problems

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    cover image ACM Conferences
    GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
    July 2008
    1814 pages
    ISBN:9781605581309
    DOI:10.1145/1389095
    • Conference Chair:
    • Conor Ryan,
    • Editor:
    • Maarten Keijzer
    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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    New York, NY, United States

    Publication History

    Published: 12 July 2008

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    Author Tags

    1. binary quadratic programming
    2. ec-memory
    3. evolutionary algorithm
    4. island model

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    • (2017)A Parallel Tabu Search for the Unconstrained Binary Quadratic Programming problem2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969360(557-564)Online publication date: 5-Jun-2017

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