Re-sampling search: A seriously simple memetic approach with a high performance

In the fashion of the Ockham's Razor principle for Memetic Computing approaches, this paper proposes an extremely simple and yet very efficient algorithm composed of two operators. The proposed approach employs a deterministic local search operator that periodically perturbs by means of a stochastic search component. The perturbation occurs by re-sampling the initial solution within the decision space. The deterministic local search is stopped by means of a precision based criterion and started over by means of the stochastic re-sampling. Although the concept of multi-start local search is not new in the optimization environment the proposed algorithm is shown to be extremely efficient on a broad set of diverse problems and competitive with complex algorithms representing the-state-of-the-art in computational intelligence optimization.

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