By Witold Kosinski

With the new tendencies in the direction of giant facts units and demanding computational energy, mixed with evolutionary algorithmic advances evolutionary computation is turning into even more suitable to perform. target of the publication is to offer contemporary advancements, leading edge rules and ideas in part of an enormous EA box.

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2006e). Using Enhanced Genetic Programming Techniques for Evolving Classiffers in the Context of Medical Diagnosis - an Empirical Study. Proceedings of the GECCO 2006 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC 2006), 2006. ; Del Re, L. & Wagner, S. (2005b) On-Line Modeling Based on Genetic Programming. Proceedings of the 1st International Workshop on Automatic Learning and Real-Time (ALaRT'05), pp. 119--130, 2005. & Del Re, L. (2006f). Quality Pre-assesment in Steel Industry using Data Based Estimators.

J. Socała, Asymptotic behaviour of the iterates of nonnegative operators on a Banach lattice, Ann. Polon. , 68 (1), (1998), 1-16. J. Socała, W. Kosiński, S. Kotowski, On asymptotic behaviour of a simple genetic algorithm (in Polish: O asymptotycznym zachowaniu prostego algorytmu genetycznego), Matematyka Stosowana. 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.

For a more detailed description of all involved algorithmic aspects the interested reader is referred to the book (Affenzeller, 2005). In principle, the SASEGASA introduces two enhancements to the basic concept of Genetic Algorithms. t. the objective function. The second concept introduces a separation of the population to increase the broadness of the search process and joins the subpopulation after their evolution in order to end up with a population including all genetic information sufficient for locating a global optimum.

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