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Ant Colony Optimization

Chapter
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 36)

Abstract

Ant colony optimization (ACO) is a metaheuristic that was originally introduced for solving combinatorial optimization problems. In this chapter we present the general description of ACO, as well as its adaptation for the application to continuous optimization problems. We apply this adaptation of ACO to optimize the weights of feed-forward neural networks for the purpose of pattern classification. As test problems we choose three data sets from the well-known PROBEN1 medical database. The experimental results show that our algorithm is comparable to specialized algorithms for feed-forward neural network training. Furthermore, the results compare favourably to the results of other general-purpose methods such as genetic algorithms.

Key words

Ant colony optimization continuous optimization pattern classification feedforward neural network training 

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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium
  2. 2.ALBCOM, LSIUniversitat Politécnica de CatalunyaBarcelonaSpain

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