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GenMin: An enhanced genetic algorithm for global optimization
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DOI：10.1016/j.cpc.2008.01.040.png)
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Abstract
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A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++.
Keywords:
genetic algorithm
genetic programming
grammatical evolution
global optimization
stochastic methods
stopping rule
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