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Additional info for Computational Intelligence, Theory and Applications: International Conference 8th Fuzzy Days in Dortmund, Germany, Sept. 29-Oct. 01, 2004 Proceedings (Advances in Soft Computing)

Sample text

Simultaneously, all particles are attracted by the best solution known by the swarm (hence, the emergence of collective behavior). The coefficients α1 and α2 are typically selected at random in each step to introduce some variability in the velocity behavior. As to ω, it is a parameter termed inertia weight, used to control the balance between exploration (high ω) and exploitation (low ω). , the swarm may find a local optimum with a large basin of attraction, and get trapped in it. , using multiple interconnected subpopulations that evolve in partial isolation (as opposed to a single panmictic population).

In: Proceedings of the Fourth IEEE International Conference on Neural Networks, Piscataway NJ, IEEE Press (1995) 1942–1948 2. : The particle swarm: Social adaptation in information processing systems. : New Ideas in Optimization. McGraw-Hill IK (1999) 379–387 3. : Particle swarm optimization: development, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, Piscataway NJ, IEEE Press (2001) 81–86 4. : Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco CA (2001) 5.

5. L. Hildebrand. Asymmetrische Evolutionsstrategien. PhD thesis, Universit¨ at Dortmund, 2001. 6. -P. Schwefel. Adaptive Mechanismen in der biologischen Evolution und ihr Einfluβ auf die Evolutionsgeschwindigkeit. Technical report, Technical University of Berlin, 1974. 7. -P. Schwefel. Evolution and Optimum Seeking. John Wiley & Sons, New York, 1995. de Summary. In order to extend fuzzy if-then rules bases, we propose to make use of a method which has been developed for the interpolation of crisp data – the multivariate spline interpolation.

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