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A New Algorithm of Evolutionary Computation: Bio-Simulated Optimization

  • Yong Wang
  • Ruijun Zhang
  • Qiumei Pu
  • Qianxing Xiong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

Genetic algorithm (GA), evolutionary programming (EP) and evolutionary strategy (ES) are called the three kinds of evolutionary computation methods. They have been widely used in many engineering fields. However, selecting individuals directly and random search lead to produce premature problem, and requirement for high precision decreases the search efficiency, these become the obstructs of application in engineering practice. This paper proposes a new algorithm of evolutionary computation, it is called bio-simulated optimization algorithm (BSO). BSO reproduces new generation through asexual propagation and sexual propagation. Here, the evolutionary operators effectively solve the problem of premature convergence. Furthermore, performance of global search and convergence are proved theoretically. Finally, Compared BSO with GA and EP in searching the optimal solution of a continuous multi-peaks function, three kinds of computation procedures are run in Matlab, the result shows that performance of BSO is superior to GA and EP.

Keywords

Genetic Algorithm Evolutionary Computation Evolutionary Generation Global Search Gene Recombination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Zhang, J.Q., Cao, Y.F., Wang, C.Q.: A Genetic Algorithm Based on Common Path for TSP. Computer Engineering and Applications 40, 58–61 (2004)Google Scholar
  2. 2.
    Holland, J.H.: Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defines Functions. Evolutionary Computation 8, 373–391 (2000)CrossRefGoogle Scholar
  3. 3.
    Fogel, L.J., Angeline, P.J., Back, T.: Evolutionary Programming V. In: Proceedings of the 5th annual conference on evolutionary programming, San Diego CA, pp. 488–496. MIT Press, Cambridge (1996); Neurocomputing  17, 133–134 (1997)Google Scholar
  4. 4.
    Zhang, J.H., Xu, X.H.: Development on Simulated Evolutionary Computing. System Engineering and Electrionic Technology 8, 44–47 (1998)Google Scholar
  5. 5.
    Rechenberg, I.: Case Studies in Evolutionary Experimentation and Computation. Computer Methods in Applied Mechanics and Engineering 186, 125–140 (2000)MATHCrossRefGoogle Scholar
  6. 6.
    Yu, W., Li, R.H.: A New Evolutionary Approach Based on Reproduction of Asexual Cells. Computer Engineering & Science 23, 7–10 (2003)Google Scholar
  7. 7.
    Tang, F., Teng, H.F., Sun, Z.G.: Schema Theorem of the Decimal-Coded Genetic Algorithm. Mini-Micro System 21, 364–367 (2000)Google Scholar
  8. 8.
    Li, H., Tang, H.W., Guo, C.H.: The Convergence Analysis of A Class of Evolution Strategies. OR Transaction 3, 79–83 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yong Wang
    • 1
  • Ruijun Zhang
    • 1
  • Qiumei Pu
    • 2
  • Qianxing Xiong
    • 2
  1. 1.School of ManagementWuhan University of Science and TechnologyWuhanChina
  2. 2.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina

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