Skip to main content
Log in

A new evolutionary algorithm based on the decimal coding

  • Published:
Wuhan University Journal of Natural Sciences

Abstract

Traditional Evolutionary Algorithm (EAs) is based on the binary code, real number code, structure code and so on. But these coding strategies have their own advantages and disadvantages for the optimization of functions. In this paper a new Decimal Coding Strategy (DCS), which is convenient for space division and alterable precision, was proposed, and the theory analysis of its implicit parallelism and convergence was also discussed. We also redesign several genetic operators for the decimal code. In order to utilize the historial information of the existing individuals in the process of evolution and avoid repeated exploring, the strategies of space shrinking and precision alterable, are adopted. Finally, the evolutionary algorithm based on decimal coding (DCEAs) was applied to the optimization of functions, the optimization of parameter, mixed-integer nonlinear programming. Comparison with traditional GAs was made and the experimental results show that the performances of DCEAS are better than the tradition GAs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Zbyszek Michalewics. Evolutionary Algorithms for Constrained Optimization.International Workshop On Evolutionary Computation, 2000,4(1):1–11.

    Article  Google Scholar 

  2. Pan Zheng-jun.Evolutionary Computation. Beijing: Tsinghua Press and Guangxi Science & Technology Press, 1998. 3–4.

    Google Scholar 

  3. Vose M D. Generalizing the Notion of Schema in Genetic Algorithms.Artifical Intelligence, 1991,50:563–565.

    Article  MathSciNet  Google Scholar 

  4. Byoung-Tak Zhang, Gerhard Paass. Convergence Properties of Incremental Bayesian Evolutionary Algorithms with Single Markov Chains.Proceedings of the 2000 Congress on Evolutionary Computation, July 16–19, California, USA, 2000, 234–245.

  5. Melanie Mitchell.An Introduction to Genetic Algorithms. Cambridge: The MIT press, 1996, 15–27.

    Google Scholar 

  6. Zeng San-you, Kang Li-shan, Ding Li-xin, A New Method of Evolutionary Algorithms for Mixed-Integer Nonlinear Optimization Problem-Automatically Constracting Search Space to Optimal Solution.Journal of Wuhan University (Natural Science Edition), 2000,46(5):554–558.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dong Wen-yong.

Additional information

Foundation item: Supported by the National Natural Science Foundation of China (No. 69703011)

Biography: Dong Wen-yong (1973-), male Ph. D. candidate, research direction: parallel algorithms, evolutionary computation, computer simulation.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wen-yong, D., Yuan-xiang, L., Bo-jin, Z. et al. A new evolutionary algorithm based on the decimal coding. Wuhan Univ. J. Nat. Sci. 7, 150–156 (2002). https://doi.org/10.1007/BF02830304

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02830304

Key words

Navigation