Digital Pattern Search and Its Hybridization with Genetic Algorithms for Bound Constrained Global Optimization

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概要

• 論文の詳細を見る

• In this paper, we present a recently developed pattern search method called Genetic Pattern Search algorithm (GPSA) for the global optimization of cost function subject to simple bounds. GPSA is a combined global optimization method using genetic algorithm (GA) and Digital Pattern Search (DPS) method, which has the digital structure represented by binary strings and guarantees convergence to stationary points from arbitrary starting points. The performance of GPSA is validated through extensive numerical experiments on a number of well known functions and on robot walking application. The optimization results confirm that GPSA is a robust and efficient global optimization method.

• （社）電子情報通信学会の論文
• 2009-02-01

著者

• Kim Sang

  Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Yonsei University College

• Kim Sang

  Pohang Univ. Sci. And Technol. (posteth) Gyungbuk Kor

• Kim Sang

  Division Of Electrical And Computer Engineering Pohang University Of Science And Technology (posteth

• KIM Nam-Geun

  Division of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTET

• PARK Youngsu

  Division of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTET

• KIM Jong-Wook

  Department of Electronics Engineering, Dong-A University

• KIM Eunsu

  Department of Electronics Engineering, Dong-A University

• Kim Nam-geun

  Division Of Electrical And Computer Engineering Pohang University Of Science And Technology (posteth

• Kim Eunsu

  Department Of Electronics Engineering Dong-a University

• Park Youngsu

  Division Of Electrical And Computer Engineering Pohang University Of Science And Technology (posteth

• Kim Jong-wook

  Department Of Electronics Engineering Dong-a University

• Kim Jong-wook

  Electrical Steel Sheet Research Group Posco Technical Research Laboratories

• Kim Sang

  Division Of Breast And Endocrine Surgery Department Of Surgery Samsung Medical Center Sungkyunkwan University School Of Medicine

• Kim Sang

  Division Of Bioscience And Bioinformatics Myongji University

• KIM Sang

  Division of Applied Life Science, Gyeongsang National University

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