Abstract
A new algorithm called Simplex Coding Genetic Algorithm (SCGA) is proposed for solving nonlinear global optimization problems. This algorithm is obtained by hybridizing genetic algorithm and simplex-based local search method called Nelder-Mead method. The efficiency of SCGA is tested on some well known functions. Comparison with other meta-heuristics indicates that the SCGA is promising.
This research was supported in part by a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, Sports and Culture of Japan.
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Hedar, AR., Fukushima, M. (2003). Simplex Coding Genetic Algorithm for the Global Optimization of Nonlinear Functions. In: Multi-Objective Programming and Goal Programming. Advances in Soft Computing, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36510-5_17
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DOI: https://doi.org/10.1007/978-3-540-36510-5_17
Publisher Name: Springer, Berlin, Heidelberg
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