Abstract
Random search methods based on evolutionary principles have been proposed in the 60’s. They did not have a major influence on mainstream optimization. We believe that this will change. The unique power of evolutionary algorithms shows up with parallel computers. Firstly, our parallel genetic algorithm PGA introduced in 1987 [MGSK87] runs especially efficient on parallel computers. Secondly, our research indicates that parallel searches with information exchange between the searches are often better than independent searches. Thus the PGA is a truly parallel algorithm which combines the hardware speed of parallel processors and the software speed of intelligent parallel searching.
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Mühlenbein, H. (1992). Parallel Genetic Algorithms in Optimization. In: Krönig, D., Lang, M. (eds) Physik und Informatik — Informatik und Physik. Informatik-Fachberichte, vol 306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77382-2_1
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