Advertisement

An analysis of synchronous and asynchronous parallel distributed genetic algorithms with structured and panmictic Islands

  • Enrique Alba
  • José Ma Troya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1586)

Abstract

In a parallel genetic algorithm (PGA) several communicating nodal GAs evolve in parallel to solve the same problem. PGAs have been traditionally used to extend the power of serial GAs since they often can be tailored to provide a larger efficiency on complex search tasks. This has led to a considerable number of different models and implementations that preclude direct comparisons and knowledge exchange. To fill this gap we begin by providing a common framework for studying PGAs. This allows us to analyze the importance of the synchronism in the migration step of parallel distributed GAs. We will show how this implementation issue affects the evaluation effort as well as the search time and the speedup. In addition, we consider popular evolution schemes of panmictic (steady-state) and structured-population (cellular) GAs for the islands. The evaluated PGAs demonstrate linear and even super-linear speedup when run in a cluster of workstations. They also show important numerical benefits when compared with their sequential counterparts. In addition, we always report lower search times for the asynchronous versions.

Keywords

Genetic Algorithm Search Time Parallel Genetic Algorithm Tentative Solution Migration Frequency 
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.
    Alba E., Aldana J. F., Troya J. M.: “Genetic Algorithms as Heuristics for Optimizing ANN Design”. In Albrecht R. F., Reeves C. R., Steele N. C. (eds.): Artificial Neural Nets and Genetic Algorithms. Springer-Verlag (1993) 683–690Google Scholar
  2. 2.
    Bäck T., Fogel D., Michalewicz Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press (1997)Google Scholar
  3. 3.
    Belding, T. C.: “The Distributed Genetic Algorithm Revisited”. In Eshelman L. J. (ed.): Proceedings of the Sixth International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, CA (1995) 114–121.Google Scholar
  4. 4.
    Cammarata G., Cavalieri S., Fichera A., Marletta L.: “Noise Prediction in Urban Traffic by a Neural Approach”. In Mira J., Cabestany J., Prieto A. (eds.): Proceedings of the International Workshop on Artificial Neural Networks, Springer-Verlag (1993) 611–619.Google Scholar
  5. 5.
    Gordon V. S., Whitley D.: “Serial and Parallel Genetic Algorithms as Function Optimizers”. In Forrest S. (ed.). Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1993) 177–183Google Scholar
  6. 6.
    Hart W. E., Baden S., Belew R. K., Kohn S.: “Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm”. In IEEE (ed.): CD-ROM IPPS97 (1997)Google Scholar
  7. 7.
    Jelasity M.: “A Wave Analysis of the Subset Sum Problem”. In Bäck T. (ed.): Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, CA (1997) 89–96Google Scholar
  8. 8.
    Romaniuk, S. G.: “Evolutionary Growth Perceptrons”. In Forrest S. (ed.): Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1992) 334–341Google Scholar
  9. 9.
    Shonkwiler R. “Parallel Genetic Algorithms”. In Forrest S. (ed.): Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA (1993) 199–205.Google Scholar
  10. 10.
    Syswerda G.: “A Study of Reproduction in Generational and Steady-State Genetic Algorithms”. In Rawlins G. (ed.): Foundations of GAs, Morgan Kaufmann (1991) 94–101Google Scholar
  11. 11.
    Whitley D.: “Cellular Genetic Algorithms”. In Forrest S. (ed.): Proceedings of the Fifth International Conference on GAs. Morgan Kaufmann, San Mateo, CA (1993) 658Google Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • Enrique Alba
    • 1
  • José Ma Troya
    • 1
  1. 1.Dpto. de Lenguajes y Ciencias de la ComputaciónUniv. de MálagaMÁLAGAEspaña

Personalised recommendations