Local Unimodal Sampling
نویسندگان
چکیده
One of the simplest ways of optimizing a problem without knowledge of its gradient, is to iteratively and randomly pick a sequence of candidate solutions that improve on the fitness. This paper shows mathematically that to exhaust the optimum of a problem, the search-range from which the candidate solutions are picked, must be decreased during an optimization run. A simple technique for doing this is presented, and is demonstrated empirically to yield rapid convergence on a simple, yet representative unimodal optimization problem.
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تاریخ انتشار 2009