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 inJournal of combinatorial optimization Vol. 30; no. 3; pp. 710 - 728
Main Authors Silva, R. M. A., Resende, M. G. C., Pardalos, P. M.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2015
Subjects
Combinatorics
Convex and Discrete Geometry
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Theory of Computation
Global optimization
Stochastic local search
Continuous optimization
Stochastic algorithm
Nonlinear programming
Biased random-key genetic algorithm
Multimodal functions
Heuristic
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ISSN1382-6905
1573-2886
DOI10.1007/s10878-013-9659-z

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Summary: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.
ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-013-9659-z