By Florin Ionescu, Dragos Arotaritei, Stefan Arghir (auth.), Andreas König, Andreas Dengel, Knut Hinkelmann, Koichi Kise, Robert J. Howlett, Lakhmi C. Jain (eds.)
The four-volume set LNAI 6881- LNAI 6884 constitutes the refereed lawsuits of the fifteenth overseas convention on Knowledge-Based clever info and Engineering platforms, KES 2011, held in Kaiserslautern, Germany, in September 2011.
Read Online or Download Knowledge-Based and Intelligent Information and Engineering Systems: 15th International Conference, KES 2011, Kaiserslautern, Germany, September 12-14, 2011, Proceedings, Part I PDF
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Extra resources for Knowledge-Based and Intelligent Information and Engineering Systems: 15th International Conference, KES 2011, Kaiserslautern, Germany, September 12-14, 2011, Proceedings, Part I
Example text
The maximum number of generations was chosen in such way that the maximum number of individual evaluations was constant for different values of M . Table 2 shows that M = 100 is the most suitable setting. Table 2. Computational effort vs population size Population size Computational Effort 10 16,058,949 20 5,613,264 50 5,805,492 100 5,038,127 200 5,727,632 500 9,250,326 1000 24,363,608 The fitness function uses the results of all the iterations to check for convergence as stated in Section 3. Finaly, the stop condition limiting the number of iterations must be chosen.
3 19 Euclidean Algorithm The most suitable CGP parameters were determined the same way as in the previous experiments. 05, nit = 40 and s = 20. Although the proposed method enables the use of results from individual iterations, it has not proved useful in this case. Therefore, the fitness function used just the overall results out of 20-point training set. The 91 % of runs led to a perfectly working solution (with the computational effort of 2,086,497). Fig. 5.
Neural Networks 5, 117–127 (1992) 2. : A new cell-based clustering method for large, high-dimensional data in data mining applications. In: Proceedings of the ACM Symposium on Applied Computing, pp. 503–507. ACM Press, New York (2002) 3. : Entropy-based subspace clustering for mining numerical data. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 84–93. ACM Press, New York (1999) 4. : Weighted cluster ensembles: Methods and analysis. TKDD 2(4) (2009) 5.