swMATH ID: 24583
Software Authors: Lu, Pingjing; Che, Yonggang; Wang, Zhenghua
Description: UMDA/S: An effective iterative compilation algorithm for parameter search. The search process is critical for iterative compilation because the large size of the search space and the cost of evaluating the candidate implementations make it infeasible to find the true optimal value of the optimization parameter by brute force. Considering it as a nonlinear global optimization problem, this paper introduces a new hybrid algorithm – UMDA/S: Univariate Marginal Distribution Algorithm with Nelder-Mead Simplex Search, which utilizes the optimization space structure and parameter dependency to find the near optimal parameter. Elitist preservation, weighted estimation and mutation are proposed to improve the performance of UMDA/S. Experimental results show the ability of UMDA/S to locate more excellent parameters, as compared to existing static methods and search algorithms.
Homepage: http://www.cai.sk/ojs/index.php/cai/article/view/137/114
Keywords: iterative compilation; optimization parameter; Nelder-Mead simplex algorithm; estimation of distribution algorithms; univariate marginal distribution algorithm
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Cited in: 1 Document

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