Vol. 25, No. 1

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Now showing 1 - 5 of 23
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    Hybrid Symbolic Regression with the Bison Seeker Algorithm
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-06-24) Merta, Jan
    This paper focuses on the use of the Bison Seeker Algorithm (BSA) in a hybrid genetic programming approach for the supervised machine learning method called symbolic regression. While the basic version of symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. This paper aims to compare the basic version of symbolic regression and hybrid version with the lifetime adaptation of individuals via the Bison Seeker Algorithm. Author also investigates the influence of the Bison Seeker Algorithm on the rate of evolution in the search for function, which fits the given input-output data. The results of the current study support the fact that the local algorithm accelerates evolution, even with a few iterations of a Bison Seeker Algorithm with small populations.
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    On the Leader Selection in the Self-Organizing Migrating Algorithm
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-06-24) Tomaszek, Lukas; Zelinka, Ivan; Chadli, Mohammed
    In this article, a novel leader selection strategy for the self-organizing migrating algorithm is introduced. This strategy replaces original AllToOne and AllToRand strategies. It is shown and statistically tested, that the new strategy outperforms the original ones. All the experiments were conducted on well known CEC 2014 benchmark functions according to the CEC competition rules and reported here.
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    Properties of Simple and Generalized Laguerre Functions for Time-delay System Approximations
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-06-24) Zsitva, Norbert
    The properties of the simple and generalized Laguerre functions for time-invariant system approximations are discussed. The expressions for these functions are presented and the differences between them are shown. The approximations heavily depend on the free parameters of the Laguerre functions. Because of this, the optimal choice of these parameters is described for both the simple and the generalized functions. This assures that the results are satisfactory. The approximations are shown on two different systems. These approximations are evaluated with the help of the quadratic error criterion. It is shown that the results differ for the two chosen systems. The reason behind this is explained with the help of the Laguerre functions' properties and the initial value theorem.
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    Modeling of Complex Systems by Means of Partial Algebras
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-06-24) Bila, Jiri; Rodríguez, Ricardo Jorge; Novak, Martin
    Complex systems are very hard to describe by some unified language and calculus. In cases when their nature is very heterogeneous is possible to use with advantage state description. Formalization of operations on the set of states usually leads to partial algebras. The work with partial algebras is rather difficult and unpractical. From this reason some methods approximating partial algebras by some more symmetrical objects are searched for. In this paper there is proposed an approximation of this algebras by free cyclic groups. Then using the combination of Matroid Theory and Ramsey theory of graph the prediction of a possible appearance of emergent situation is executed. Data and knowledge used in the paper for the demonstration of developed method application are from the field of Ecology.
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    Ant Colony Optimisation for Performing Computational Task in Cellular Automata
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-06-24) Bidlo, Michal; Korgo, Jakub
    A method is presented for the design of cellular automata rules by means of ant algorithms. In particular, Elitist Ant System and a~modified MAX-MIN Ant System are applied to search for transition functions of 1D cellular automata that are able to calculate squares of given input values. It will be shown that the proposed MAX-MIN Ant System can perform significantly better than the standard variant of Elitist Ant System. In particular, in the most advanced case study, the ant algorithm showed an ability to design a~complete set of elementary cellular automata rules that fulfil the required square calculations. Some selected results will be presented and their features discussed.