Model-Based Randomized Methods for Global Optimization
نویسندگان
چکیده
We survey some randomized search methods for global optimization that are based on sampling from an underlying probability distribution “model” on the solution space. In this approach, the probability model is updated iteratively after evaluating the performance of the samples at each iteration. Such model-based methods include estimation of distribution algorithms (EDAs), the cross-entropy method (CEM), and the recently proposed model reference adaptive search (MRAS). Keywords— Global optimization, Cross-entropy method, Estimation of distribution algorithm, Model reference adaptive search
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