Abstract
In this paper we will formulate a framework for a parallel population based search process: an Abstract Cellular Genetic Algorithm (ACGA). Using the ACGA as a template, various parallel search algorithms can be formulated, e.g. parallel Genetic Algorithms and parallel Simulated Annealing. As a case study we will investigate the influence of locality on the behaviour of a Cellular Genetic Algorithm (CGA), that is constructed according to this framework. A neighbourhood structure is imposed upon the population, which results in overlapping local cell-populations. Using varying neighbourhood sizes, we will discuss experiments with CGAs ranging from maximally local to effectively global. The CGA has been applied to a load balancing problem: the NP-hard problem of mapping a process graph onto a processor topology in parallel finite element simulations.
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Schoneveld, A., de Ronde, J.F., Sloot, P.M.A., Kaandorp, J.A. (1996). A parallel cellular genetic algorithm used in finite element simulation. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1017
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DOI: https://doi.org/10.1007/3-540-61723-X_1017
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