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A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm

Author

Listed:
  • R. M. A. Silva

    (Universidade Federal de Pernambuco)

  • M. G. C. Resende

    (AT&T Labs Research)

  • P. M. Pardalos

    (University of Florida)

Abstract

This paper describes libbrkga, a GNU-style dynamic shared Python/C++ library of the biased random-key genetic algorithm (BRKGA) for bound constrained global optimization. BRKGA (J Heuristics 17:487–525, 2011b) is a general search metaheuristic for finding optimal or near-optimal solutions to hard optimization problems. It is derived from the random-key genetic algorithm of Bean (ORSA J Comput 6:154–160, 1994), differing in the way solutions are combined to produce offspring. After a brief introduction to the BRKGA, including a description of the local search procedure used in its decoder, we show how to download, install, configure, and use the library through an illustrative example.

Suggested Citation

  • R. M. A. Silva & M. G. C. Resende & P. M. Pardalos, 2015. "A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 710-728, October.
    • Handle: RePEc:spr:jcomop:v:30:y:2015:i:3:d:10.1007_s10878-013-9659-z
    DOI: 10.1007/s10878-013-9659-z
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    References listed on IDEAS

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    1. Hirsch, M.J. & Pardalos, P.M. & Resende, M.G.C., 2010. "Speeding up continuous GRASP," European Journal of Operational Research, Elsevier, vol. 205(3), pages 507-521, September.
    2. Mauricio Resende, 2012. "Biased random-key genetic algorithms with applications in telecommunications," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 130-153, April.
    3. Gonçalves, José Fernando & Resende, Mauricio G.C., 2013. "A biased random key genetic algorithm for 2D and 3D bin packing problems," International Journal of Production Economics, Elsevier, vol. 145(2), pages 500-510.
    4. M. Ericsson & M.G.C. Resende & P.M. Pardalos, 2002. "A Genetic Algorithm for the Weight Setting Problem in OSPF Routing," Journal of Combinatorial Optimization, Springer, vol. 6(3), pages 299-333, September.
    5. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
    Full references (including those not matched with items on IDEAS)

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