Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique

R. Yang and I. Douglas
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R. Yang: University of Manchester
I. Douglas: University of Manchester

Journal of Optimization Theory and Applications, 1998, vol. 98, issue 2, No 9, 449-465

Abstract: Abstract Genetic algorithms are known to be efficient for global optimizing. However, they are not well suited to perform finely-tuned local searches and are prone to converge prematurely before the best solution has been found. This paper uses genetic diversity measurements to prevent premature convergence and a hybridizing genetic algorithm with simplex downhill method to speed up convergence. Three case studies show the procedure to be efficient, tough, and robust.

Keywords: Genetic algorithms; genetic diversity; simplified coding; global optimizing; local tuning (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (2)

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DOI: 10.1023/A:1022697719738

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