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Elements of an algorithm for optimizing a parameter-structural neural network

Abstract

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

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The impatience mechanism as a diversity maintaining and saddle crossing strategy

References Barabasz, B., Gajda-Zagórska, E., Migórski, S., Paszyński, M., Schaefer, R. and Smołka, M. (2014). A hybrid algorithm for solving inverse problems in elasticity, International Journal of Applied Mathematics and Computer Science 24(4): 865-886, DOI: 10.2478/amcs-2014-0064. Chen, T., He, J., Chen, G. and Yao, X. (2010). Choosing selection pressure for wide-gap problems, Theoretical Computer Science 411(6): 926-934. Chorazyczewski, A. and Galar, R. (1998). Visualization of evolutionary adaptation in Rn

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The Impact Of Increased Stability And Efficiency Through Automatic Control System For A Steam Power Plant

References 1. Marian Popescu, Ilie Borcoși, “Increasing productivity at the level of the power plant by optimizing the distributed systems and hierarchical systems”, Annals of the University Constantin Brancusi, Series Engeneering, ISSN 1842-4856. No.3/2013, pp. 241-244, (2013) 2. Ion Marian Popescu, “Necessity Implementation of advanced control algorithms in the complex processes in the energy industry”, Annals of the University of Craiova, Series: Automation, Computer, Electronics and Mechatronics, ISSN 1841-0626, Vol.11 (38), issue 1, (2014

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Application of Information Technologies and Algorithms in Ship Passage Planning

References 1. Kumar, A., D. Kumar, S. Jarial. A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering. – Cybernetics and Information Technologies, Vol. 17 , 2017, No 3, pp. 3-28. 2. Balabanov, T., I. Zankinski, M. Barova. Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of Distributed Evolutionary Algorithms. – Cybernetics and Information Technologies, Vol. 16 , 2016, No 1, pp. 80-88. 3. Eiben, S. Introduction to Evolutionary Computing. Ch. II. e-Book. 2015, p. 1. 4

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A study on meme propagation in multimemetic algorithms

References Alba, E. (2005). Parallel Metaheuristics: A New Class of Algorithms, Wiley-Interscience, Hoboken, NJ. Alba, E. and Luque, G. (2004). Growth curves and takeover time in evolutionary algorithms, in K. Deb (Ed.), GECCO 2004, Lecture Notes in Computer Science, Vol. 3102, Springer-Verlag, Berlin/Heidelberg, pp. 864-876. Cantu-Paz, E. (2000). Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, Norwell, MA. Chakhlevitch, K. and Cowling, P. (2008). Hyperheuristics

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Simulation-Based Analysis of Fitness Landscape in Optimisation

References Jones T. Evolutionary Algorithms, Fitness Landscapes and Search. - Albuquerque: The University of New Mexico, 1995, 224 p. Langdon W.B., Poli R. Foundations of Genetic Programming. - Berlin, Heidelberg: Springer-Vorlag, 2002, 260 p. Reeves C.R., Rowe J.E. Genetic Algorithms - Principles and Perspectives. A Guide to GA Theory. - Springer, 2002, 344 p. Vassilev V.K., Fogarty T.C., Miller J.F. Information Characteristics and the Structure of

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Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving

. & Semenkin E. (2015). Cooperative Multi-objective Genetic Algorithm with Parallel Implementation. Advances in Swarm and Computational Intelligence , LNCS vol. 9140. pp. 471–478. Springer Nature. Crainic, T. G., Toulouse, M. (2010). Parallel metaheuristics. In Handbook of metaheuristics , pp. 497–541. Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-1-4419-1665-5_17 Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation , 6 (2

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Fine Tuning of Agent-Based Evolutionary Computing

References [1] P. Adamidis. Parallel evolutionary algorithms: A review. In Proceedings of the 4th Hellenic-European Conference on Computer Mathematics and its Applications (HERCMA 1998), Athens, Greece, 1998. [2] T. Bäck and H.-P. Schwefel. Evolutionary computation: An overview. In T. Fukuda and T. Furuhashi, editors, Proceedings of the Third IEEE Conference on Evolutionary Computation. IEEE Press, 1996. [3] A. Byrski, M. Kisiel-Dorohinicki, and E. Nawarecki. Agent-based evolution of neural network architecture. In M. Hamza, editor, Proc. of the

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The Model of Fitness in a Heterogeneous Environment on Reaction Norms

REFERENCES Buckley, L. B., Urban, M. C., Angilletta, M. J., Crozier, L. G., Rissler, L. J., Sears, M. W. (2010). Can mechanism inform species’ distribution models? Ecol. Lett. , 13 (8), 1041–1054. Chevin, L.-M., Gallet, R., Gomulkiewicz, R., Holt, R. D., Fellous, S. (2013). Phenotypic plasticity in evolutionary rescue experiments. Philos.Trans. Royal Soc. B: Biol. Sci. , 368 (1610), 1–12. Chevin, L.-M., Lande, R., Mace, G. M. (2010). Adaptation, plasticity, and extinction in a changing environment: Towards a predictive theory. PLoS Biol

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Modeling the Impacts of Climate Change on Phytogeographical Units. A Case Study of the Moesz Line

(1), 5-39. (in Hungarian) Mátyás, Cs., Berki, I., Czúcz, B., Gálos, B., Móricz, N., Rasztovits, E. 2010. Future of beech in Southeast Europe from the perspective of evolutionary ecology. Acta Silv. Lign. Hung . 6 (1), 91-110. Moesz, G. 1911. Adatok Bars vármegye flórájához (Supplementary data of Bars county). Botanikai Közlemények 10 (5-6), 171-185. (in Hungarian) Ogawa-Onishi, Y., Berry, P.M., Tanaka, N. 2010. Assessing the potential impacts of climate change and their conservation implications in Japan: A case study of conifers

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