✦ LIBER ✦

A new dynamical evolutionary algorithm based on statistical mechanics

✍ Scribed by YuanXiang Li; XiuFen Zou; LiShan Kang; Michalewicz Zbigniew

Publisher: Springer
Year: 2003
Tongue: English
Weight: 776 KB
Volume: 18
Category: Article
ISSN: 1000-9000
DOI: 10.1007/bf02948906

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Finding global minima with a new dynamic

Finding global minima with a new dynamical evolutionary algorithm

✍ Zou Xiu-fen; Kang Li-shan; Li Yuan-xiang; Chen Yu-ping 📂 Article 📅 2002 🏛 Wuhan University 🌐 English ⚖ 298 KB

A new evolutionary algorithm based on th

A new evolutionary algorithm based on the decimal coding

✍ Dong Wen-yong; Li Yuan-xiang; Zheng Bo-jin; Zen San-you; Zhang Jin-bo 📂 Article 📅 2002 🏛 Wuhan University 🌐 English ⚖ 508 KB

Statistical mechanics of a dynamical sys

Statistical mechanics of a dynamical system based on Conway's game of Life

✍ L. S. Schulman; P. E. Seiden 📂 Article 📅 1978 🏛 Springer 🌐 English ⚖ 1023 KB

A new algorithm for on-line handwriting

A new algorithm for on-line handwriting signature verification based on evolutionary computation

✍ Zheng Jianbin; Zhu Guangxi 📂 Article 📅 2006 🏛 Wuhan University 🌐 English ⚖ 582 KB

A word alignment model based on multiobj

A word alignment model based on multiobjective evolutionary algorithms

✍ Yidong Chen; Xiaodong Shi; Changle Zhou; Qingyang Hong 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 450 KB

translation (SMT) Word alignment a b s t r a c t Word alignment is a key task in statistical machine translation (SMT). This paper presents a novel model for this task. In this model, word alignment is considered as a multiobjective optimization problem and solved based on the non-dominated sorting

A fast evolutionary pursuit algorithm ba

A fast evolutionary pursuit algorithm based on linearly combining vectors

✍ Qijun Zhao; Hongtao Lu; David Zhang 📂 Article 📅 2006 🏛 Elsevier Science 🌐 English ⚖ 157 KB

In this paper, we propose a fast Evolutionary pursuit algorithm. It searches for optimal discriminant vectors through linearly combining standard bases. Experiments on the Yale and ORL databases verify its effectiveness and enhanced efficiency.