NDLI: Genetic algorithms encoding study and a sufficient convergence condition of GAs

Content Provider	IEEE Xplore Digital Library
Author	Meng, Q.C. Feng, T.J. Chen, Z. Zhou, C.J. Bo, J.H.
Copyright Year	1999
Description	Author affiliation: Dept. of Electr. Eng., Ocean Univ. of Qingdao, China (Meng, Q.C.)
Abstract	This paper studies the encoding techniques of genetic algorithms and a sufficient convergence condition on genetic encoding in genetic algorithms is presented. Some new categories of genetic codes are defined, such as uniform code, bias code, tri-sector code and symmetric codes, etc, and they are applied in some problem optimizations and a robotic problem solution. These codes have found their application in developing some special and powerful genetic algorithms. For example, based on symmetric code theory, new genetic strategy, GASC: Genetic Algorithm with Symmetric Code, is developed. In the paper, some key definitions on encoding are given out, such as living-block, dead-block, link, fix-link, living-population, as well as some operations on genetic, such as bit-transposition, bit-and, population-and, member-and, etc. Some of our research shows that genetic encoding techniques have a very important influence on the performance of genetic algorithms. The convergence speed of genetic algorithms with some specially developed codes will be much faster than conventional genetic algorithms. That is very significant for finding more applications of genetic algorithms, as, in many cases, genetic algorithm applications are limited by their convergence speed.
Starting Page	649
Ending Page	652
File Size	325367
Page Count	4
File Format	PDF
ISBN	0780357310
ISSN	1062922X
DOI	10.1109/ICSMC.1999.814168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-10-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Encoding Convergence Robots Artificial neural networks Fuzzy logic Genetic engineering Oceans Intelligent systems Marine technology
Content Type	Text
Resource Type	Article

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