Skip to main content
SpringerLink
Log in

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

Journal of Combinatorial Optimization Aims and scope Submit manuscript

Cite this 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  • Ackley DH (1987) A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  • Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, New York

    Google Scholar 

  • Bean JC (1994) Genetic algorithms and random Keys for sequencing and optimization. ORSA J Comput 6:154–160

    Article  Google Scholar 

  • Calcote J (2010) Autotools: a practitioner’s guide to GNU autoconf, automake, and libtool. No Starch Press, San Francisco

    Google Scholar 

  • Ericsson M, Resende MGC, Pardalos PM (2002) A genetic algorithm for the weight setting problem in OSPF routing. J Comb Optim 6:299–333

    Article  MathSciNet  Google Scholar 

  • Gonçalves JF, Almeida J (2002) A hybrid genetic algorithm for assembly line balancing. J Heuristics 8:629–642

    Article  Google Scholar 

  • Gonçalves JF, Resende MGC (2004) An evolutionary algorithm for manufacturing cell formation. Comput Ind Eng 47:247–273

    Article  Google Scholar 

  • Gonçalves JF, Resende MGC (2011a) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17:487–525

    Article  Google Scholar 

  • Gonçalves JF, Resende MGC (2011b) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17(5):487–525

    Article  Google Scholar 

  • Gonçalves JF, Resende MGC (2012) A parallel multi-population biased random-key genetic algorithm for a container loading problem. Comput Oper Res 39(2):179–190

    Article  MathSciNet  Google Scholar 

  • Gonçalves JF, Resende MGC (2013) A biased random key genetic algorithm for 2D and 3D bin packing problems. Int J Prod Econ 145:500–510

    Article  Google Scholar 

  • Hirsch MJ, Meneses CN, Pardalos PM, Resende MGC (2007) Global optimization by continuous grasp. Optim Lett 1:201–212

    Article  MathSciNet  MATH  Google Scholar 

  • Hirsch MJ, Pardalos PM, Resende MGC (2010) Speeding up continuous GRASP. J Oper Res 205:507–521

    Article  Google Scholar 

  • Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul 8(1):3–30

    Article  Google Scholar 

  • McGuire P (2007) Getting started with pyparsing. O’Reilly Media, Sebastopol

    Google Scholar 

  • Resende MGC (2012) Biased random-key genetic algorithms with applications in telecommunications. TOP 20(1):130–153

    Article  MathSciNet  Google Scholar 

  • Resende MGC, Toso Rodrigo F, Silva Ricardo MA (2012) A biased random-key genetic algorithm for the steiner triple covering problem. Optim Lett 6(4):605–619

    Article  MathSciNet  MATH  Google Scholar 

  • Silva RMA, Resende MGC, Pardalos PM, Gonçalves JF (2012) Biased random-key genetic algorithm for bound-constrained global optimization. In: Aloise D, Hansen P, Rocha C (eds) Proceedings of the global optimization workshop, pp 133–136

  • Spears WM, DeJong KA (1991) On the virtues of parameterized uniform crossover. In: Proceedings of the fourth international conference on genetic algorithms, pp 230–236

  • SymPy (2011) URL. http://sympy.org/. Accessed 11 Jul 2011

  • Toso RF, Resende MGC (2012) A C++ application programming interface for biased random-key genetic algorithms. Technical report, Algorithms and Optimization Research Department, AT &T Labs Research

  • van Rossum G, Drake FL Jr (eds) (2010a) Extending and embedding Python, Release 2.7. Python Software Foundation, Wolfeboro Falls

    Google Scholar 

  • van Rossum G, Drake FL Jr (eds) (2010b) Python/C API reference manual, release 2.7. Python Software Foundation, Wolfeboro Falls

    Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. Centro de Informática (CIn), Universidade Federal de Pernambuco, Av. Prof. Luís Freire s/n, Cidade Universitária, Recife, PE, Brazil

    R. M. A. Silva

  2. Algorithms and Optimization Research Department, AT&T Labs Research, 180 Park Avenue, Room C241, Florham Park, NJ, 07932, USA

    M. G. C. Resende

  3. Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, Gainesville, FL, 32611, USA

    P. M. Pardalos

Authors
  1. R. M. A. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. G. C. Resende
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. M. Pardalos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. G. C. Resende.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Silva, R.M.A., Resende, M.G.C. & Pardalos, P.M. A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm. J Comb Optim 30, 710–728 (2015). https://doi.org/10.1007/s10878-013-9659-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-013-9659-z

Keywords

search

Navigation