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Journal of Combinatorial Optimization

, Volume 30, Issue 3, pp 710–728 | Cite as

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

  • R. M. A. Silva
  • M. G. C. Resende
  • P. M. Pardalos
Article

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.

Keywords

Biased random-key genetic algorithm Global optimization Multimodal functions Continuous optimization  Heuristic Stochastic algorithm Stochastic local search Nonlinear programming 

Notes

Acknowledgments

The research of R. M. A Silva was partially supported by the Brazilian National Council for Scientic and Technological Development (CNPq), the Foundation for Support of Research of the State of Minas Gerais, Brazil (FAPEMIG), Coordination for the Improvement of Higher Education Personnel, Brazil (CAPES), Foundation for the Support of Development of the Federal University of Pernambuco, Brazil (FADE), the Ofce for Research and Graduate Studies of the Federal University of Pernambuco (PROPESQ), and the Foundation for Support of Science and Technology of the State of Pernambuco (FACEPE).

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • R. M. A. Silva
    • 1
  • M. G. C. Resende
    • 2
  • P. M. Pardalos
    • 3
  1. 1.Centro de Informática (CIn)Universidade Federal de PernambucoRecifeBrazil
  2. 2.Algorithms and Optimization Research DepartmentAT&T Labs ResearchFlorham ParkUSA
  3. 3.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA

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