Classification-Rule Discovery with an Ant Colony Algorithm

Classification-Rule Discovery with an Ant Colony Algorithm

Rafael S. Parpinelli (CEFET-PR, Brazil), Heitor S. Lopes (CEFET-PR, Brazil) and Alex A. Freitas (University of Kent, UK)
DOI: 10.4018/978-1-59140-553-5.ch074
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Abstract

Ant colony optimization (ACO) is a relatively new computational intelligence paradigm inspired by the behaviour of natural ants (Bonabeau, Dorigo & Theraulaz, 1999). The natural behaviour of ants that we are interested in is the following. Ants often find the shortest path between a food source and the nest of the colony without using visual information. In order to exchange information about which path should be followed, ants communicate with each other by means of a chemical substance called pheromone. As ants move, a certain amount of pheromone is dropped on the ground, creating a pheromone trail. The more ants follow a given trail, the more attractive that trail becomes to be followed by other ants. This process involves a loop of positive feedback, in which the probability that an ant chooses a path is proportional to the number of ants that have already passed by that path.

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