By Rami N. Khushaba, Ahmed Al-Ani, Akram AlSukker, Adel Al-Jumaily (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Maurice Clerc, Thomas Stützle, Alan F. T. Winfield (eds.)
This e-book constitutes the refereed lawsuits of the sixth overseas Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2008, held in Brussels, Belgium, in September 2008.
The 17 revised complete papers, 24 revised brief papers, and 10 prolonged abstracts provided have been conscientiously reviewed and chosen from ninety one submissions. The papers disguise theoretical and foundational features of computational intelligence and similar disciplines with precise specialize in swarm intelligence and are dedicated to behavioral versions of social bugs and new algorithmic ways, empirical and theoretical examine in swarm intelligence, purposes comparable to ant colony optimization or particle swarm optimization, and theoretical and experimental learn in swarm robotics systems.
Read Online or Download Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings PDF
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Extra resources for Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings
3 Local Search In all the cases, the local search clearly increases the interpretability results with respect to the ‘ACS’ version. Furthermore, the inclusion of this extension allows to reach the goal of ﬁnding solutions, on average, as interpretable as those of the initial fuzzy models (in fact, more interpretable than them). Of course, the extra-evaluation derived from the local search increases the computational cost. However, the ﬁnal averaged computational cost of the algorithm shows diﬀerent results depending on the dimensionality of the rule bases.
Dorigo et al. ): ANTS 2008, LNCS 5217, pp. 37–47, 2008. S. C. H. Moore Concept Diﬃculty Combining the diﬃculty of modeling nonlinear attribute interactions with the challenge of attribute selection yields for this domain what Goldberg  calls a needle-in-a-haystack problem. That is, there may be a particular combination of SNPs that together with the right nonlinear function are a signiﬁcant predictor of disease susceptibility. Considered individually they may not look any diﬀerent than thousands of other SNPs not involved in the disease process.
Here we begin to develop a probabilistic search wrapper which can be integrated into the publicly available Multifactor Dimensionality Reduction (MDR) software. But is ant colony optimization suitable for a problem like this? Without expert knowledge the answer would seem to be no. There is no reason to expect that an ACO or any other wrapper method will perform better than a random attribute selector because there are no building blocks for this problem when accuracy is used as a metric of quality.
Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings by Rami N. Khushaba, Ahmed Al-Ani, Akram AlSukker, Adel Al-Jumaily (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Maurice Clerc, Thomas Stützle, Alan F. T. Winfield (eds.)