Skip to main content
Springer Nature Link
Log in

A Genetic and Evolutionary Programming Environment with Spatially Structured Populations and Built-In Parallelism

  • Conference paper
  • First Online:
Engineering of Intelligent Systems (IEA/AIE 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2070))

  • 750 Accesses

Abstract

The recent development of the Genetic and Evolutionary Computation field lead to a kaleidoscope of approaches to problem solving, which are based on a common background. These shared principles are used in order to develop a programming environment that enhances modularity, in terms of software design and implementation. The system’s core encapsulates the main features of the Genetic and Evolutionary Algorithms, by identifying the entities at stake and implementing them as hierarchies of software modules. This architecture is enriched with the parallelization of the algorithms, based on spatially structured populations, following coarse-grained (Island Model) and fine-grained (Neighborhood Model) strategies. A distributed physical implementation, under the PVM environment, running in a local network, is described.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. A. Geist, A. Beguelin, J. Dongarra, W. Jiang, R. Manchek, and V. Sunderam. PVM: Parallel Virtual Machine: A User’s Guide and Tutorial for Networked Parallel Computing. MIT Press, 1994.

    Google Scholar 

  2. J. E. Baker. Reducing Bias and Inefficiency in the Selection Algorithm. In J. Grenfenstette, editor, Proceedings of the Second International Conference on Genetic Algorithms and their Applications. Lawrence Erlbaum Associates, 1987.

    Google Scholar 

  3. E. Cantu-Paz. A survey of parallel genetic algorithms. IlliGAL Report 97003, University of Ilinois at Urbana-Champaign, Urbana, IL, may 1997.

    Google Scholar 

  4. E. Cantu-Paz. Migration policies, selection pressure, and parallel genetic algorithms. IlliGAL Report 99015, University of Ilinois at Urbana-Champaign, Urbana, IL, jun 1999.

    Google Scholar 

  5. P. Cortez, M. Rocha, J. Machado, and J. Neves. An evolutionary and connectionist approach for time series forecasting. In Proceedings of Thirteenth International Conference on i Systems Engineering-ICSE 99, Las Vegas, USA, aug 1999.

    Google Scholar 

  6. Francisco Herrera and Manuel Lozano. Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(1):43–63, apr 2000.

    Article  Google Scholar 

  7. H. Mühlenbein. Evolution in time and space-the parallel genetic algorithm. In G. Rawlins, editor, Foundations of Genetic Algorithms, pages 316–337. Morgan-Kaufman, 1991.

    Google Scholar 

  8. Masaharu Munetomo, Yoshiaki Takai, and Yoshiharu Sato. An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms. Technical Report HIER-IS-9301, Hokkaido University, 1993.

    Google Scholar 

  9. J. Neves, M. Rocha, H. Rodrigues, M. Biscaia, and J. Alves. Adaptive Strategies and the Design of Evolutionary Applications. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO99), Orlando, Florida, USA, 1999.

    Google Scholar 

  10. M. Rocha, P. Cortez, and J. Neves. The relationship between learning and evolution in static and in dynamic environments. In C. Fyfe, editor, Proceedings of the 2nd ICSC Symposium on Engineering of Intelligent Systems (EIS’2000), pages 377–383. ICSC Academic Press, 2000.

    Google Scholar 

  11. M. Rocha, C. Vilela, P. Cortez, and J. Neves. Viewing scheduling problems through genetic and evolutionary algorithms. In Proceedings of the BioSP3 workshop, 2000.

    Google Scholar 

  12. M. Rocha, C. Vilela, and J. Neves. A study of order based genetic and evolutionary algorithms in combinatorial optimization problems. In R. Loganantharaj, G. Palm, and M. Ali, editors, Proceedings of the 13th IEA/AIE’2000. Springer, 2000.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Informática, Universidade do Minho, Braga, PORTUGAL

    Miguel Rocha, Filipe Pereira, Sónia Afonso & José Neves

Authors
  1. Miguel Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Filipe Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sónia Afonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Neves
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Hungarian Academy of Sciences, Intelligent Manufacturing and Business Processes Computer and Automation Research Institute, Kende utca 13-17, 1111, Budapest, Hungary

    László Monostori  & József Váncza  & 

  2. Department of Computer Science 601 University Drive, Southwest Texas State University, San Marcos, TX, 78666-4616, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rocha, M., Pereira, F., Afonso, S., Neves, J. (2001). A Genetic and Evolutionary Programming Environment with Spatially Structured Populations and Built-In Parallelism. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_43

Download citation

  • DOI: https://doi.org/10.1007/3-540-45517-5_43

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42219-8

  • Online ISBN: 978-3-540-45517-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation