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Another extension is to define typical mathematical function types to integrate not
only one function as class but a few functions combined under a similar type of func-
tions to get a general set of parameters. This might lead to a better generalization for
the learned classifier. The problem of this task is to find a general problem class which
defines typical kinds of mathematical functions.
References
1. van den Bergh, F., Engelbrecht, A.: A new locally convergent particle swarm optimiser. In:
2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, p. 6 (October
2002)
2. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial
Systems, 1st edn. Oxford University Press, USA (1999)
3. Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Swarm
Intelligence Symposium, pp. 120-127 (2007)
4. Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a mul-
tidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58-73
(2002)
5. Eberhart, R., Kennedy, J.: A new optimizer using part swarm theory. In: Proceedings of the
Sixth International Symposium on Micro Maschine and Human Science, pp. 39-43 (1995)
6. Hoos, H., Stützle, T.: Stochastic Local Search: Foundations & Applications. Morgan Kauf-
mann Publishers Inc., San Francisco (2004)
7. Hutter, F., Hoos, H.H., Stutzle, T.: Automatic algorithm configuration based on local search.
In: Proceedings of the Twenty-Second Conference on Artifical Intelligence (AAAI 2007),
pp. 1152-1157 (2007)
8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE
International Conference on Neural Network, Perth, Australia, pp. 1942-1948 (1995)
9. Leyton-Brown, K., Nudelman, E., Shoham, Y.: Learning the Empirical Hardness of Opti-
mization Problems: The Case of Combinatorial Auctions. In: Van Hentenryck, P. (ed.) CP
2002. LNCS, vol. 2470, pp. 91-100. Springer, Heidelberg (2002)
10. Pant, M., Thangaraj, R., Singh, V.P.: Particle swarm optimization using gaussian inertia
weight. In: International Conference on Conference on Computational Intelligence and Mul-
timedia Applications, vol. 1, pp. 97-102 (2007)
11. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco
(1993)
12. Shi, Y., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: Porto, V.W.,
Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591-600. Springer, Heidelberg (1998)
13. Talbi, E.G.: Metaheuristics: From design to implementation. Wiley, Hoboken (2009),
14. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter
selection. Inf. Process. Lett. 85(6), 317-325 (2003)
15. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd
edn. Morgan Kaufmann, San Francisco (2005)
16. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Transactions on
Evolutionary Computation 1(1), 67-82 (1997)
