Negative Correlation Learning in the Estimation of Distribution Algorithms for Combinatorial Optimization

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概要

• 論文の詳細を見る

• This article introduces the Coincidence Algorithm (COIN) to solve several multimodal puzzles. COIN is an algorithm in the category of Estimation of Distribution Algorithms (EDAs) that makes use of probabilistic models to generate solutions. The model of COIN is a joint probability table of adjacent events (coincidence) derived from the population of candidate solutions. A unique characteristic of COIN is the ability to learn from a negative sample. Various experiments show that learning from a negative example helps to prevent premature convergence, promotes diversity and preserves good building blocks.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Chongstitvatana Prabhas

  Department Of Computer Engineering Faculty Of Engineering Chulalongkorn University

• WATTANAPORNPROM Warin

  Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University

関連論文

• Positive Span of Force and Torque Components in Three-Dimensional Four-Finger Force-Closure Grasps
• Negative Correlation Learning in the Estimation of Distribution Algorithms for Combinatorial Optimization

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