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