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Automatic Design of Ant Algorithms with Grammatical Evolution

  • Jorge Tavares
  • Francisco B. Pereira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7244)

Abstract

We propose a Grammatical Evolution approach to the automatic design of Ant Colony Optimization algorithms. The grammar adopted by this framework has the ability to guide the learning of novel architectures, by rearranging components regularly found on human designed variants. Results obtained with several TSP instances show that the evolved algorithmic strategies are effective, exhibit a good generalization capability and are competitive with human designed variants.

Keywords

Training Instance Grammatical Evolution Travel Salesperson Problem Pheromone Matrix Daemon Action 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jorge Tavares
    • 1
  • Francisco B. Pereira
    • 1
    • 2
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.ISECCoimbraPortugal

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