By Witold Kosinski

With the hot developments in the direction of giant information units and demanding computational strength, mixed with evolutionary algorithmic advances evolutionary computation is changing into even more correct to perform. goal of the booklet is to give contemporary advancements, cutting edge principles and ideas in part of a major EA box.

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Lasota, Asymptotic properties of semigroups of Markov operators (in Polish), Matematyka Stosowana. Matematyka dla Społeczeństwa,PTM, Warszawa, 3(45), 2002, 39-51. P. Kieś and Z. Michalewicz, Foundations of genetic algorithms (in Polish), Matematyka Stosowana. Matematyka dla Społeczeństwa , PTM Warszawa 1(44), 2000, 68{91. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, 1996. M. D. Vose, The Simple Genetic Algorithm: Foundation and Theory, MIT Press, Cambridge, MA, 1999.

Furthermore, optimal thresholds are also identified automatically on the basis of a misclassification matrix storing information about how to weight misclassification dependent on the respective classes involved. This matrix is initially set so that all misclassifications are weighted equally; in various different applications it can be necessary to manipulate this weighting as it is, for example in the context of medical data analysis, Evolutionary Systems Identification: New Algorithmic Concepts and Applications 43 more critical misclassifying a diseased patient as not diseased than vice versa.

Matematyka dla Społeczeństwa , PTM, Warszawa,6 (47), 2005, 70-86. J. Socała, W. Kosiński, Lower-bound function method in the converegence analysis of genetic algorithms, (in Polish: Zastosowanie metody funkcji dolnej do badania zbieżności algorytmów genetycznych, Matematyka Stosowana. Matematyka dla Spo leczeństwa, PTM, Warszawwa, 8 (49), 2007 , 33-44. J. Socała, W. 366, in print. J. Socała, Markovian approach to genetic algorithms, under preparation. Kotowski, W. Kosiński, Z. Michalewicz, J. Nowicki, B.

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