UMDA/S: An Effective Iterative Compilation Algorithm for Parameter Search

Authors

  • Pingjing Lu
  • Yonggang Che
  • Zhenghua Wang

Keywords:

Iterative compilation, optimization parameter, Nelder-Mead simplex algorithm, estimation of distribution algorithms, univariate marginal distribution alegorithm

Abstract

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.

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Author Biographies

Pingjing Lu

National Laboratory for Parallel and Distributed Processing
School of Computer
National University of Defense Technology
Changsha 410073, China

Yonggang Che

National Laboratory for Parallel and Distributed Processing
School of Computer
National University of Defense Technology
Changsha 410073, China

Zhenghua Wang

National Laboratory for Parallel and Distributed Processing
School of Computer
National University of Defense Technology
Changsha 410073, China

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Published

2012-01-26

How to Cite

Lu, P., Che, Y., & Wang, Z. (2012). UMDA/S: An Effective Iterative Compilation Algorithm for Parameter Search. Computing and Informatics, 29(6+), 1159–1179. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/137