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European Conference on Genetic Programming

EuroGP 2004: Genetic Programming pp 89–100Cite as

A Practical Approach to Evolving Concurrent Programs

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3003))

Abstract

Although much research has been devoted to devising genetic programming systems that are capable of running the evolutionary process in parallel, thereby improving execution speed, comparatively little effort has been expended on evolving programs which are themselves inherently concurrent. A suggested reason for this is that the vast number of parallel execution paths that are open to exploration during the fitness evaluation of population members renders evolutionary computation prohibitively expensive. We have therefore investigated the potential for minimising this expense by using a far more limited exploration of the execution state space to guide evolution. The approach, involving the definition of sets of schedulings to enable a variety of execution interleavings to be specified, has been applied to the classic ‘dining philosophers’ problem, and has been found to evolve solutions that are as good as those created by human programmers.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Liverpool Liverpool L69 3BX, United Kingdom

    David Jackson

Authors
  1. David Jackson
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Editor information

Editors and Affiliations

  1. No Affiliation,  

    Maarten Keijzer

  2. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA

    Una-May O’Reilly

  3. University of Essex Department of Computing and Electronic Systems, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Simon Lucas

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Department of Computer Science, University of Idaho, ID 83844-1010, Moscow,  

    Terence Soule

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© 2004 Springer-Verlag Berlin Heidelberg

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Jackson, D. (2004). A Practical Approach to Evolving Concurrent Programs. In: Keijzer, M., O’Reilly, UM., Lucas, S., Costa, E., Soule, T. (eds) Genetic Programming. EuroGP 2004. Lecture Notes in Computer Science, vol 3003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24650-3_9

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  • DOI: https://doi.org/10.1007/978-3-540-24650-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21346-8

  • Online ISBN: 978-3-540-24650-3

  • eBook Packages: Springer Book Archive

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