Model Reference Adaptive Search: A New Approach to Global Optimization
نویسندگان
چکیده
We present a randomized algorithm called Model Reference Adaptive Search (MRAS) for solving global optimization problems. The algorithm generates at each iteration a group of candidate solutions according to a parameterized probabilistic model. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be biased toward the region containing high quality solutions. The parameter updating procedure in MRAS is guided by a sequence of implicit reference models that will eventually converge to a model producing only the optimal solutions. We establish global convergence of MRAS in both continuous and combinatorial domains. Numerical studies are also carried out to demonstrate the effectiveness of the algorithm.
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