A Real-Coded Genetic Algorithm Taking Account of the Weighted Mean of the Population

被引：0

作者：

Nakashima, Naotoshi ^{[1
]}

Nagata, Yuichi ^{[1
]}

Ono, Isao ^{[1
]}

机构：

[1] Tokyo Inst Technol, Tokyo, Japan

来源：

PROCEEDINGS OF THE EIGHTEENTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 18TH '13) | 2013年

关键词：

function optimization; genetic algorithms; real-coded GAs; AREX/JGG;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Continuous function optimization is an important problem in science and engineering. The real-coded genetic algorithm (RCGA) has shown good performance in continuous function optimization. AREX/JGG is one of the most promising RCGAs. However, we believe that AREX/JGG has two problems in terms of search efficiency. In this paper, we propose a new RCGA that overcomes the problems of AREX/JGG. In order to examine the effectiveness of the proposed RCGA, we compared the performance of the proposed RCGA with that of AREX/JGG on several benchmark problems in which initial populations do not cover the optimal points. As the result, we confirmed that the proposed RCGA succeeded in finding the optimal points faster than AREX/JGG.

引用

页码：325 / 328

页数：4

共 4 条

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[2] [Anonymous], 2009, P 11 ANN C GEN EV CO
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Herrera, F
Lozano, M
Sánchez, AM
[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2003, 18 (03) : 309 - 338
[4] A population-based, parent centric procedure for constrained real-parameter optimization
Sinha, Ankur
Srinivasan, Aravind
Deb, Kalyanmoy
[J]. 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, : 239 - +

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