Download PDF by Rudolf F. Albrecht (auth.), Bernd Reusch (eds.): Computational Intelligence: Theory and Applications

By Rudolf F. Albrecht (auth.), Bernd Reusch (eds.)

ISBN-10: 354066050X

ISBN-13: 9783540660507

Fuzzy Days in Dortmund have been held for the 1st time in 1991. at the beginning, the con­ ference used to be meant for scientists and practitioners as a platform for discussions on concept and alertness of fuzzy common sense. Early on, synergetic hyperlinks with neural networks have been integrated and the convention advanced steadily to embody the whole spectrum of what's now known as Computational Intelligence (CI). consequently, it appeared logical to release the 4th Fuzzy Days in 1994 as a convention for CI—one of the world's first meetings that includes fuzzy good judgment, neural networks and evolu­ tionary algorithms jointly in a single occasion. Following this profitable culture, the sixth Fuzzy Days' goal is to supply a global discussion board for reporting major effects at the conception and alertness of Cl-methods. once more, now we have obtained a amazing variety of papers. i need to precise my gratitude to all who've been attracted to providing their paintings in the framework of this convention and to the participants of the programme committee for his or her beneficial paintings (in this version each one paper was once reviewed through 5 referees). particularly, I desire to thank all keynote and instructional audio system for his or her dedication. final yet no longer least, i'm obliged to the Deutsche Forschun- gemeinschaft and Kommunalverband Ruhrgebiet for his or her monetary support.

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Additional info for Computational Intelligence: Theory and Applications International Conference, 6th Fuzzy Days Dortmund, Germany, May 25–28 1999 Proceedings

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E. for the evaluation of solutions generated by the genetic algorithm. There are compared the selection operator based on dominance features (Pareto set) and that of scalar function based. 1 Introduction Genetic algorithms have widely been used in optimization tasks, and there are only a few works that use genetic algorithms to find rules. One of these studies is described [2] as follows: a set of solutions consists of various encoded rules: attribute\=valueB; attributes =valueC.

The chromatic number x ( ^ ) of graph G (if it is known) is given in column xThe remaining columns denote heuristic BRE and the different mutation operators. Notice, that each mutation operator improves on the results obtained by heuristic BRE. As can be seen mutation MUT2 performs best on the given benchmark set. Experiments have shown that using two or more mutations in the EA (we show MUT1-I-MUT2 in Table 1) does not result in large gains. In a next series of experiments we give an impression on the influence of the iterated greedy heuristic IG.

45 7 Results The following experiments have been performed: 1. 2. 3. 4. 5. Comparisons Comparisons Comparisons Comparisons Comparisons with patterns, whose evaluation is rating>7 (see Fig. 2, Appendix); with patterns whose evaluation is rating=10 ; with all examples of the training set; with " bad" patterns whose evaluation is rating<4 ; with patterns whose evaluation is rating=l (see Fig. 3, Appendix); The rate of good solutions in the population with regard to each of criterion: NumberOfGoodSolutiomOnCriterion _ i RateOfGoodSolutionsOnCriterion _ i = NumberOfSolutionsOnPopulation 8 (13) Conclusions As compared with patterns that have ratings 8, 9, and 10 there are strings in the training set that are closer to the objective, that is why this searching path is the shortest (see Fig.

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Computational Intelligence: Theory and Applications International Conference, 6th Fuzzy Days Dortmund, Germany, May 25–28 1999 Proceedings by Rudolf F. Albrecht (auth.), Bernd Reusch (eds.)


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