Abstract
A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima exist in the hierarchical optimization problem, 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 used to divide the hierarchical structure. Dividing large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of the large-scale system then becomes possible due to information sharing concerning Pareto optimum solutions for the sub-problems.
