Multimodal optimization of thermal histories

Practical optimization problems often require the location of multiple solutions. In this context, we present a model, based on a genetic algorithm (GA) for the optimization of thermal histories. Since in classical GA, the population of individuals converges over time to a single optimum, even within a multimodal domain, the proposed approach uses a niching method, such as the sharing heuristic, that enables a GA to locate multiple optima of this multimodal problem. Some empirical results are then presented and compared with another model.

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