Prospects for Computational Steering of Evolutionary Computation

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Abstract

Currently, evolutionary computation (EC) typically takes place in batch mode: algorithms are run autonomously, with the user providing little or no intervention or guidance. Although it is rarely possible to specify in advance, on the basis of EC theory, the optimal evolutionary algorithm for a particular problem, it seems likely that experienced EC practitioners possess considerable tacit knowledge of how evolutionary algorithms work. In situations such as this, computational steering (ongoing, informed user intervention in the execution of an otherwise autonomous computational process) has been profitably exploited to improve performance and generate insights into computational processes. In this short paper, prospects for the computational steering of evolutionary computation are assessed, and a prototype example of computational steering applied to a coevolutionary algorithm is presented.
Original languageEnglish
Title of host publicationArtificial Life VIII: Workshop Proceedings of the Eighth International Conference on Artificial Life
Subtitle of host publicationBeyond Fitness - Visualising Evolution
EditorsE. Bilotta, Gro D., T. Smith, T. Lenaerts, S. Bullock, H. H. Lund, J. Bird, R. Watson, P. Pantano, L. Pagliarini, H. Abbass, R. Standish, M. Bedau
Place of PublicationSydney, Australia
PublisherMassachusetts Institute of Technology (MIT) Press
Pages131-137
Number of pages7
Publication statusPublished - Dec 2002
Event8th International Conference on Artificial Life (ALife VIII) - Sydney, Australia
Duration: 9 Dec 200213 Dec 2002

Conference

Conference8th International Conference on Artificial Life (ALife VIII)
CountryAustralia
CitySydney
Period9/12/0213/12/02

Keywords

  • evolutionary computation
  • coevolution
  • evolutionary steering
  • virulence
  • visualisation
  • reduced virulence
  • virulence adaptation
  • disengagement
  • coevolutionary disengagement
  • coevolutionary dynamics

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