NDLI: Dynamic diversity control by injecting artificial chromosomes for solving TSP problems

Content Provider	IEEE Xplore Digital Library
Author	Pei-Chann Chang Wei-Hsiu Huang Liu, J.Y.-C. Cycer Chen Chang-Jung Ting
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. Manage., Yuan Ze Univ., Taoyuan (Pei-Chann Chang)
Abstract	The applications of genetic algorithms (GAs) in solving combinatorial problems are frequently faced with a problem of early convergence and the evolutionary processes are often trapped in a local but not global optimum. This premature convergence occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature, plenty of work has been investigated to introduce new methods and operators in order to overcome this essential problem of genetic algorithms. As these methods and the belonging operators are rather problem specific in general. In this research, we take a different approach by observing the progress of the evolutionary process and when the diversity of the population dropping below a threshold level then artificial chromosomes with high diversity will be introduced to increase the average diversity level thus to ensure the process can jump out the local optimum. The proposed method is implemented independently of the problem characteristics and can be applied to improve the global convergence behavior of genetic algorithms. The experimental results using TSP instances show that the proposed approach is very effective in preventing the premature convergence when compared with the earlier approaches.
Starting Page	542
Ending Page	549
File Size	421675
Page Count	8
File Format	PDF
ISBN	9781424418220
DOI	10.1109/CEC.2008.4630849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological cells Hamming distance Genetic algorithms Evolutionary computation Cities and towns Convergence Length measurement
Content Type	Text
Resource Type	Article

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