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Suche nach „[S.] [Ovaska]“ hat 4 Publikationen gefunden
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    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    S. Ovaska, Bernhard Sick, et al.

    Periodical Switching Between Related Goals for Improving Evolvability to a Fixed Goal in Multi-Objective Problems

    Information Sciences, vol. 179, no. 23, pp. 4046-4056

    2009

    Abstract anzeigen

    Evolutionary computation plays a principal role in intelligent design automation. Evolutionary approaches have discovered novel and patentable designs. Nonetheless, evolutionary techniques may lead to designs that lack robustness. This critical issue is strongly connected to the concept of evolvability. In nature, highly evolvable species tend to be found in rapidly changing environments. Such species can be considered robust against environmental changes. Consequently, to create robust engineering designs it could be beneficial to use variable, rather than fixed, fitness criteria. In this paper, we study the performance of an evolutionary programming algorithm with periodical switching between goals, which are selected randomly from a set of related goals. It is shown by a dual-objective filter optimization example that altering goals may improve evolvability to a fixed goal by enhancing the dynamics of solution population, and guiding the search to areas where improved solutions are likely to be found. Our reference algorithm with a single goal is able to find solutions with competitive fitness, but these solutions are results of premature convergence, because they are poorly evolvable. By using the same algorithm with switching goals, we can extend the productive search length considerably; both the fitness and robustness of such designs are improved.

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    Bernhard Sick, S. Ovaska

    Fusion of Soft and Hard Computing: Multi-Dimensional Categorization of Computationally Intelligent Hybrid Systems

    Neural Computing & Applications, vol. 16, no. 2, pp. 125-137

    2007

    Abstract anzeigen

    The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    S. Ovaska, Bernhard Sick

    Fusion of Soft Computing and Hard Computing: Applications and Research Opportunities

    Chapter 3

    Computational Intelligence: Principles and Practice; IEEE Computational Intelligence Society, Piscataway; 2006 (keynote article of the International Joint Conference on Neural Networks 2006, Vancouver)

    2006

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    Bernhard Sick, S. Ovaska

    Fusion of Soft and Hard Computing Techniques: A Multi-Dimensional Categorization Scheme

    Proceedings of the IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications (SMCia/05); Espoo, Finnland; 28.-30.06.2005

    2005