Spatial Genetic Algorithm and Its Parallel Implementation I
نویسنده
چکیده
The spatial genetic algorithm (SGA) is presented. Locality is realized by mapping GA population on a cellular automata. The role of neighborhood in genetic search is shown by comparing SGA with the parallel recornbinative simulated annealing (PRSA) approach proposed by Mahfoud and Goldberg in [1]. It appears, that not optimized SGA outdoes PRSA in loose and is only slightly worse in tight optimization problems defined in [1]. However, because of high potential parallel speedup for SGA and the possibility of domain decomposition due to SGA locality, the efficient parallel realization of SGA seems to be more perspective than PRSA. The SGA opens the way for investigations of more sophisticated neighborhood definitions based on the lattice gas and molecular dynamics paradigms, which are well scalable (in parallel computing sense) and can simulate the natural space-and-time environment lacking in pure GAs.
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