A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm
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 o...
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| Published in | Journal of combinatorial optimization Vol. 30; no. 3; pp. 710 - 728 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.10.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1382-6905 1573-2886 |
| DOI | 10.1007/s10878-013-9659-z |
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| 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. |
|---|---|
| AbstractList | 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. |
| Author | Silva, R. M. A. Resende, M. G. C. Pardalos, P. M. |
| Author_xml | – sequence: 1 givenname: R. M. A. surname: Silva fullname: Silva, R. M. A. organization: Centro de Informática (CIn), Universidade Federal de Pernambuco – sequence: 2 givenname: M. G. C. surname: Resende fullname: Resende, M. G. C. email: mgcr@research.att.com organization: Algorithms and Optimization Research Department, AT&T Labs Research – sequence: 3 givenname: P. M. surname: Pardalos fullname: Pardalos, P. M. organization: Department of Industrial and Systems Engineering, University of Florida |
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| Cites_doi | 10.1016/j.ijpe.2013.04.019 10.1007/s11750-011-0176-x 10.1007/978-1-4613-1997-9 10.1007/s11590-011-0285-3 10.1007/s10732-010-9143-1 10.1023/A:1020377910258 10.1016/j.ejor.2010.02.009 10.1007/s11590-006-0021-6 10.1023/A:1014852026591 10.1145/272991.272995 10.1016/j.cie.2004.07.003 10.1093/oso/9780195099713.001.0001 10.1016/j.cor.2011.03.009 10.1287/ijoc.6.2.154 |
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| Keywords | Global optimization Stochastic local search Continuous optimization Stochastic algorithm Nonlinear programming Biased random-key genetic algorithm Multimodal functions Heuristic |
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| References | 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 GonçalvesJFAlmeidaJA hybrid genetic algorithm for assembly line balancingJ Heuristics2002862964210.1023/A:1020377910258 HirschMJMenesesCNPardalosPMResendeMGCGlobal optimization by continuous graspOptim Lett20071201212235760110.1007/s11590-006-0021-61246.11159 GonçalvesJFResendeMGCBiased random-key genetic algorithms for combinatorial optimizationJ Heuristics20111748752510.1007/s10732-010-9143-1 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 SymPy (2011) URL. http://sympy.org/. Accessed 11 Jul 2011 GonçalvesJFResendeMGCA biased random key genetic algorithm for 2D and 3D bin packing problemsInt J Prod Econ201314550051010.1016/j.ijpe.2013.04.019 EricssonMResendeMGCPardalosPMA genetic algorithm for the weight setting problem in OSPF routingJ Comb Optim20026299333192021310.1023/A:1014852026591 van RossumGDrakeFLJrExtending and embedding Python, Release 2.72010Wolfeboro FallsPython Software Foundation BeanJCGenetic algorithms and random Keys for sequencing and optimizationORSA J Comput1994615416010.1287/ijoc.6.2.154 McGuirePGetting started with pyparsing2007SebastopolO’Reilly Media BäckTEvolutionary algorithms in theory and practice1996New YorkOxford University Press GonçalvesJFResendeMGCA parallel multi-population biased random-key genetic algorithm for a container loading problemComput Oper Res2012392179190280405910.1016/j.cor.2011.03.009 HirschMJPardalosPMResendeMGCSpeeding up continuous GRASPJ Oper Res201020550752110.1016/j.ejor.2010.02.009 ResendeMGCBiased random-key genetic algorithms with applications in telecommunicationsTOP2012201130153291352110.1007/s11750-011-0176-x MatsumotoMNishimuraTMersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generatorACM Trans Model Comput Simul19988133010.1145/272991.272995 AckleyDHA connectionist machine for genetic hillclimbing1987 BostonKluwer Academic Publishers10.1007/978-1-4613-1997-9 GonçalvesJFResendeMGCBiased random-key genetic algorithms for combinatorial optimizationJ Heuristics201117548752510.1007/s10732-010-9143-1 CalcoteJAutotools: a practitioner’s guide to GNU autoconf, automake, and libtool2010San FranciscoNo Starch Press van RossumGDrakeFLJrPython/C API reference manual, release 2.72010Wolfeboro FallsPython Software Foundation ResendeMGCTosoRodrigo FSilvaRicardo MAA biased random-key genetic algorithm for the steiner triple covering problemOptim Lett201264605619290224910.1007/s11590-011-0285-31262.90151 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 GonçalvesJFResendeMGCAn evolutionary algorithm for manufacturing cell formationComput Ind Eng20044724727310.1016/j.cie.2004.07.003 M Ericsson (9659_CR5) 2002; 6 JF Gonçalves (9659_CR7) 2004; 47 MGC Resende (9659_CR17) 2012; 6 DH Ackley (9659_CR1) 1987 M Matsumoto (9659_CR14) 1998; 8 9659_CR21 MJ Hirsch (9659_CR13) 2010; 205 P McGuire (9659_CR15) 2007 JC Bean (9659_CR3) 1994; 6 T Bäck (9659_CR2) 1996 JF Gonçalves (9659_CR6) 2002; 8 9659_CR20 J Calcote (9659_CR4) 2010 JF Gonçalves (9659_CR9) 2011; 17 9659_CR19 JF Gonçalves (9659_CR10) 2012; 39 9659_CR18 (9659_CR23) 2010 JF Gonçalves (9659_CR8) 2011; 17 (9659_CR22) 2010 JF Gonçalves (9659_CR11) 2013; 145 MJ Hirsch (9659_CR12) 2007; 1 MGC Resende (9659_CR16) 2012; 20 |
| References_xml | – reference: van RossumGDrakeFLJrPython/C API reference manual, release 2.72010Wolfeboro FallsPython Software Foundation – reference: HirschMJPardalosPMResendeMGCSpeeding up continuous GRASPJ Oper Res201020550752110.1016/j.ejor.2010.02.009 – reference: SymPy (2011) URL. http://sympy.org/. Accessed 11 Jul 2011 – reference: GonçalvesJFResendeMGCBiased random-key genetic algorithms for combinatorial optimizationJ Heuristics20111748752510.1007/s10732-010-9143-1 – reference: EricssonMResendeMGCPardalosPMA genetic algorithm for the weight setting problem in OSPF routingJ Comb Optim20026299333192021310.1023/A:1014852026591 – reference: GonçalvesJFResendeMGCBiased random-key genetic algorithms for combinatorial optimizationJ Heuristics201117548752510.1007/s10732-010-9143-1 – reference: GonçalvesJFResendeMGCA biased random key genetic algorithm for 2D and 3D bin packing problemsInt J Prod Econ201314550051010.1016/j.ijpe.2013.04.019 – reference: AckleyDHA connectionist machine for genetic hillclimbing1987 BostonKluwer Academic Publishers10.1007/978-1-4613-1997-9 – reference: HirschMJMenesesCNPardalosPMResendeMGCGlobal optimization by continuous graspOptim Lett20071201212235760110.1007/s11590-006-0021-61246.11159 – reference: 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 – reference: GonçalvesJFAlmeidaJA hybrid genetic algorithm for assembly line balancingJ Heuristics2002862964210.1023/A:1020377910258 – reference: ResendeMGCBiased random-key genetic algorithms with applications in telecommunicationsTOP2012201130153291352110.1007/s11750-011-0176-x – reference: CalcoteJAutotools: a practitioner’s guide to GNU autoconf, automake, and libtool2010San FranciscoNo Starch Press – reference: ResendeMGCTosoRodrigo FSilvaRicardo MAA biased random-key genetic algorithm for the steiner triple covering problemOptim Lett201264605619290224910.1007/s11590-011-0285-31262.90151 – reference: BeanJCGenetic algorithms and random Keys for sequencing and optimizationORSA J Comput1994615416010.1287/ijoc.6.2.154 – reference: MatsumotoMNishimuraTMersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generatorACM Trans Model Comput Simul19988133010.1145/272991.272995 – reference: 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 – reference: van RossumGDrakeFLJrExtending and embedding Python, Release 2.72010Wolfeboro FallsPython Software Foundation – reference: GonçalvesJFResendeMGCAn evolutionary algorithm for manufacturing cell formationComput Ind Eng20044724727310.1016/j.cie.2004.07.003 – reference: 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 – reference: BäckTEvolutionary algorithms in theory and practice1996New YorkOxford University Press – reference: McGuirePGetting started with pyparsing2007SebastopolO’Reilly Media – reference: GonçalvesJFResendeMGCA parallel multi-population biased random-key genetic algorithm for a container loading problemComput Oper Res2012392179190280405910.1016/j.cor.2011.03.009 – volume: 145 start-page: 500 year: 2013 ident: 9659_CR11 publication-title: Int J Prod Econ doi: 10.1016/j.ijpe.2013.04.019 – volume: 20 start-page: 130 issue: 1 year: 2012 ident: 9659_CR16 publication-title: TOP doi: 10.1007/s11750-011-0176-x – volume-title: A connectionist machine for genetic hillclimbing year: 1987 ident: 9659_CR1 doi: 10.1007/978-1-4613-1997-9 – volume-title: Getting started with pyparsing year: 2007 ident: 9659_CR15 – volume: 6 start-page: 605 issue: 4 year: 2012 ident: 9659_CR17 publication-title: Optim Lett doi: 10.1007/s11590-011-0285-3 – volume: 17 start-page: 487 issue: 5 year: 2011 ident: 9659_CR9 publication-title: J Heuristics doi: 10.1007/s10732-010-9143-1 – volume-title: Python/C API reference manual, release 2.7 year: 2010 ident: 9659_CR23 – volume: 8 start-page: 629 year: 2002 ident: 9659_CR6 publication-title: J Heuristics doi: 10.1023/A:1020377910258 – volume: 205 start-page: 507 year: 2010 ident: 9659_CR13 publication-title: J Oper Res doi: 10.1016/j.ejor.2010.02.009 – ident: 9659_CR18 – ident: 9659_CR19 – volume-title: Autotools: a practitioner’s guide to GNU autoconf, automake, and libtool year: 2010 ident: 9659_CR4 – volume: 1 start-page: 201 year: 2007 ident: 9659_CR12 publication-title: Optim Lett doi: 10.1007/s11590-006-0021-6 – volume: 6 start-page: 299 year: 2002 ident: 9659_CR5 publication-title: J Comb Optim doi: 10.1023/A:1014852026591 – volume: 17 start-page: 487 year: 2011 ident: 9659_CR8 publication-title: J Heuristics doi: 10.1007/s10732-010-9143-1 – volume: 8 start-page: 3 issue: 1 year: 1998 ident: 9659_CR14 publication-title: ACM Trans Model Comput Simul doi: 10.1145/272991.272995 – volume: 47 start-page: 247 year: 2004 ident: 9659_CR7 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2004.07.003 – volume-title: Evolutionary algorithms in theory and practice year: 1996 ident: 9659_CR2 doi: 10.1093/oso/9780195099713.001.0001 – volume: 39 start-page: 179 issue: 2 year: 2012 ident: 9659_CR10 publication-title: Comput Oper Res doi: 10.1016/j.cor.2011.03.009 – volume: 6 start-page: 154 year: 1994 ident: 9659_CR3 publication-title: ORSA J Comput doi: 10.1287/ijoc.6.2.154 – ident: 9659_CR20 – ident: 9659_CR21 – volume-title: Extending and embedding Python, Release 2.7 year: 2010 ident: 9659_CR22 |
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| SubjectTerms | Combinatorics Convex and Discrete Geometry Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Theory of Computation |
| Title | A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm |
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