Parallel Genetic Algorithm for Constrained Clustering

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概要

• 論文の詳細を見る

• In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.

• 社団法人電子情報通信学会の論文
• 1997-02-25

著者

• HAN Myung-Mook

  Faculty of Engineering, Osaka City University

• TATSUMI Shoji

  Faculty of Engineering, Osaka City University

• KITAMURA Yasuhiko

  Faculty of Engineering, Osaka City University

• OKUMOTO Takaaki

  Faculty of Engineering, Osaka Institute of Technology

• Han Myung-mook

  Osaka City University

• Kitamura Yasuhiko

  Faculty Of Engineering Osaka City University

• Tatsumi Shoji

  Faculty Of Engineering Osaka City University

• Tatsumi S

  Osaka City Univ. Osaka‐shi Jpn

• Okumoto T

  Osaka Institute Of Technology

関連論文

• Parallel Genetic Algorithm for Constrained Clustering
• Parallel Genetic Algorithms Based on a Multiprocessor System FIN and Its Application
• Boltzmann Machine and Parallel Genetic Algorithms Based on the Fin
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• Q-ee Learning : A Novel Q-Learning Method with Exploitation and Exploration
• An Accelerated k-Certainty Exploration Method
• Applying Genetic Algorithm to Conceptual Clustering
• Algorithms for Matrix Multiplication and the FFT on a Processor Array with Separable Buses(Regular Section)
• Solving an All-Pairs Shortest Paths Problem on a Processor Array with Separable Buses
• A Two-dimensional Recognition System by Cellular Automaton Array Using the Context-free Grammer
• Optimal Task Allocation by Circulation Board Protocol
• A Pattern Defect Inspection Method by Grayscale Image Comparison without Precise Image Alignment

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