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

R. M. A. Silva (), M. G. C. Resende () and P. M. Pardalos ()
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R. M. A. Silva: Universidade Federal de Pernambuco
M. G. C. Resende: AT&T Labs Research
P. M. Pardalos: University of Florida

Journal of Combinatorial Optimization, 2015, vol. 30, issue 3, No 19, 710-728

Abstract: 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 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10878-013-9659-z

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