A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima almost always arise, however genetic algorithms that have a hierarchical genotype can be applied to treat such problems directly. Relations between the structural components are analyzed and this information is used to partition the hierarchical structure. Partitioning large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of large-scale systems then becomes possible due to information sharing of Pareto optimum solutions for the sub-problems.
Hierarchical Parallel Processes of Genetic Algorithms for Design Optimization of Large-Scale Products
Contributed by the Design Automation Committee for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 2002; revised June 2003. Associate Editor: G. Fadel.
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Yoshimura, M., and Izui, K. (May 5, 2004). "Hierarchical Parallel Processes of Genetic Algorithms for Design Optimization of Large-Scale Products ." ASME. J. Mech. Des. March 2004; 126(2): 217–224. https://doi.org/10.1115/1.1666889
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