Advertisement

Comparative Analysis of Two Distribution Building Optimization Algorithms

  • Pavel Galushin
  • Olga Semenkina
  • Andrey Shabalov
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)

Abstract

This paper proposes the modification of genetic algorithm, which uses genetic operators, effecting not on particular solutions, but on the probabilities distribution of solution vector’s components. This paper also compares reliability and efficiency of basic algorithm and proposed modification using the set of benchmark functions and real-world problem of dynamic scheduling of truck painting.

Keywords

Probability Vector Benchmark Function Tournament Selection Dynamic Schedule Reproduction Probability 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aho, A.V., Lam, M.S., Sethi, R., Ullman, J.D.: Compilers: Principles, techniques, and tools, 2nd edn. Addison-Wesley (2007)Google Scholar
  2. 2.
    Baluja, S.: Population-Based Incremental Learning: A method for integrating Genetic Search Based Function Optimization and Competitive Learning, Technical Report. Carnegie Mellon University, Pittsburgh (1994)Google Scholar
  3. 3.
    Knuth, D.: The Art of Computer Programming, 3rd edn. Seminumerical algorithms, vol. 2. Addison-Wesley (1997)Google Scholar
  4. 4.
    Kurose, J.F., Simha, R.: A microeconomic approach to optimal resource allocation in distributed computer systems. IEEE Trans. Computers. 38, 707–717 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pavel Galushin
    • 1
  • Olga Semenkina
    • 1
  • Andrey Shabalov
    • 1
  1. 1.Institute of Computer Science and TelecommunicationSiberian State Aerospace UniversityKrasnoyarskRussia

Personalised recommendations