Learning monitoring strategies: A difficult genetic programming application

Marc S. Atkin, Paul R. Cohen

Research output: Contribution to conferencePaper

2 Scopus citations

Abstract

Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviors. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex.

Original languageEnglish (US)
Pages328-332
Number of pages5
StatePublished - Dec 1 1994
EventProceedings of the 1st IEEE Conference on Evolutionary Computation. Part 1 (of 2) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1st IEEE Conference on Evolutionary Computation. Part 1 (of 2)
CityOrlando, FL, USA
Period6/27/946/29/94

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Atkin, M. S., & Cohen, P. R. (1994). Learning monitoring strategies: A difficult genetic programming application. 328-332. Paper presented at Proceedings of the 1st IEEE Conference on Evolutionary Computation. Part 1 (of 2), Orlando, FL, USA, .