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Author:

Du, Xiu-Li (Du, Xiu-Li.) (Scholars:杜修力) | Han, Ling (Han, Ling.) | Jiang, Li-Ping (Jiang, Li-Ping.)

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Abstract:

It is necessary to obtain corresponding solutions to evaluating the fitness of all individuals of every generation of the population and to analyze the solutions by using Genetic Algorithm. When the scale of problem is large, the calculation of genetic algorithm will be so enormous that it ean not be used in practice. However, a new method called empirical genetic algorithm is proposed in the paper. It decrease the number to analyze the solution and increase the efficiency of the genetic algorithm, in which the fitness of most individuals of every generation of the population are estimated by the empirical Neural Network. The calculation results from six classical test functions show that the method is efficient.

Keyword:

Global optimization Neural networks Genetic algorithms

Author Community:

  • [ 1 ] [Du, Xiu-Li]Key Laboratory of Urban Security and Disaster Engineering, Beijing University of Technology, Beijing 100022, China
  • [ 2 ] [Han, Ling]Beijing Institute of Architectural Design, Beijing 100045, China
  • [ 3 ] [Jiang, Li-Ping]Shandong Provincial Academy of Building Research, Jinan 250031, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2006

Issue: 11

Volume: 32

Page: 992-995

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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