Abstract
A new model of parallel distributed genetic algorithm, Dual Individual Distributed Genetic Algorithm (DuDGA), is proposed. This algorithm frees the user from having to set some parameters because each island of Distributed Genetic Algorithm (DGA) has only two indi-viduals. DuDGA can automatically determine crossover rate, migration rate, and island number. Moreover, compared to simple GA and DGA methods, DuDGA can find better solutions with fewer analyses. Capa-bility and effectiveness of the DuDGA method are discussed using four typical numerical test functions.
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CITATION STYLE
Hiroyasu, T., Miki, M., Hamasaki, M., & Tanimura, Y. (2000). A new model of parallel distributed genetic algorithms for cluster systems: Dual individual DGAs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1940, pp. 374–383). Springer Verlag. https://doi.org/10.1007/3-540-39999-2_36
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