Artificial Evolution: 7th International Conference, by Denis Robilliard, Sébastien Mahler, Dominique Verhaghe,

By Denis Robilliard, Sébastien Mahler, Dominique Verhaghe, Cyril Fonlupt (auth.), El-Ghazali Talbi, Pierre Liardet, Pierre Collet, Evelyne Lutton, Marc Schoenauer (eds.)

This publication constitutes the completely refereed post-proceedings of the seventh overseas convention on man made Evolution, EA 2005, held in Lille, France, in October 2005.

The 26 revised complete papers offered have been rigorously reviewed and chosen from seventy eight submissions. The papers hide all facets of synthetic evolution: genetic programming, laptop studying, combinatorial optimization, co-evolution, self-assembling, synthetic existence and bioinformatics.

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Additional resources for Artificial Evolution: 7th International Conference, Evolution Artificielle, EA 2005, Lille, France, October 26-28, 2005, Revised Selected Papers

Example text

In this dataset the original Ant-Miner already obtained a very short average rule length, close to 1, and so it is perfectly acceptable that this result cannot be significantly improved. In the other four datasets, the hybrid rule pruner has significantly lowered the rule length, and so significantly improved rule comprehensibility, taking into account the standard deviations, for all tested values of r. The results were particularly good in the Web-mining datasets, as can be seen in Table 3, where rule length is reduced to less than half the rule length associated with the original Ant-Miner in most cases.

Each of those datasets contains a number of attributes greater than the number of examples. Recall that the hybrid rule pruner selects r number of terms, and terms are selected with probability based on their information gain. In very sparse datasets such as the Web-mining datasets, the values of the information gain of the attributes are not very “reliable”, since they are prone to overfitting issues. As a result, the hybrid rule pruner has difficulty in selecting r relevant terms based on the computed information gain values.

These experiments have two main goals. First, evaluating how sensitive the performance of Ant-Miner with the hybrid pruner is with respect to different values of the parameter r. Second, comparing the performance of the hybrid rule pruner with the performance of the original Ant-Miner’s rule pruner. The experiments used mainly 5 datasets – as summarised in Table 1, detailing key statistics (the number of examples, attributes and classes) for each dataset. The Chess and the House-votes datasets have been taken from the well-known UCI Machine Learning dataset repository – see [6] for details about these data sets.

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