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An Investigation into the Use of Different Search Strategies with Grammatical Evolution

  • John O’Sullivan
  • Conor Ryan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2278)

Abstract

We present an investigation into the performance of Grammatical Evolution using a number of different search strategies, Simulated Annealing, Hill Climbing, Random Search and Genetic Algorithms. Comparative results on three different problems are examined. We analyse the nature of the search spaces presented by these problems and offer an explanation for the contrasting performance of each of the search strategies. Our results show that Genetic Algorithms provide a consistent level of performance across all three problems successfully coping with sensitivity of the system to discrete changes in the selection of productions from the associated grammar.

Keywords

Genetic Algorithm Simulated Annealing Crossover Operator Hill Climbing Symbolic Regression 
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.

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References

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    Mitchell, M.and Holland, J.H. 1993. When will a Genetic Algorthm Outperform Hill Climbing? Technical report, Santa Fe Institute.Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • John O’Sullivan
    • 1
  • Conor Ryan
    • 1
  1. 1.Department of Computer Science and Information SystemsUniversity of LimerickIreland

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