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Designing Pheromone Update Strategies with Strongly Typed Genetic Programming

  • Jorge Tavares
  • Francisco B. Pereira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

Ant Colony algorithms are population-based methods widely used in combinatorial optimization problems. We propose a strongly typed genetic programming approach to automatically evolve the communication mechanism that allows ants to cooperatively solve a given problem. Results obtained with several TSP instances show that the evolved pheromone update strategies are effective, exhibit a good generalization capability and are competitive with human designed variants.

Keywords

Travel Salesperson Problem Pheromone Level Genetic Program Algorithm Pheromone Matrix Standard Genetic Program 
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 2011

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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