Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6858990 | International Journal of Approximate Reasoning | 2013 | 18 Pages |
Abstract
The effectiveness of the synergy has been tested on twelve datasets. Using non-parametric statistical tests we show that, although achieving statistically equivalent solutions, the adoption of this synergy allows saving up to 97.38% of the execution time with respect to a state-of-the-art multi-objective evolutionary approach which learns rules from scratch.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Michela Antonelli, Pietro Ducange, Francesco Marcelloni,