Abstract
Be stars are characterized by prominent emission lines in their spectrum. In the past research has attention been given to creation a feature extraction method for classification of Be stars with focusing on the automated classification of Be stars based on typical shapes of their emission lines. The aim was to design a reduced, specific set of features characterizing and discriminating the shapes of Be lines. In this chapter we discuss possibility to create in an evolutionary way the model of spectra of Be stars. We focus on the evolutionary synthesis of the mathematical models of Be stars based on typical shapes of their emission lines. Analytical programming powered by classical random as well as chaotic random-like number generator is used here. Experimental data are used from the archive of the Astronomical Institute of the Academy of Sciences of the Czech Republic. Interpretation and explanation of analysis is given and discussed in this chapter.
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References
Thizy, O.: Classical Be Stars High Resolution Spectroscopy, Society for Astronomical Sciences Annual Symposium, pp. 27–49. http://adsabs.harvard.edu/abs/2008SASS%8527%8549T, Provided by the SAO/NASA Astrophysics Data System (2008)
Debosscher, J.: Automated Classification of Variable Stars: Application to the OGLE and CoRoT Databases. Institute of Astronomy, Faculty of Sciences, Catholic University of Leuven, Leuven (2009)
Bromova, P., Skoda, P., Zendulka, J.: Wavelet based feature extraction for clustering of Be stars. In: Proceedings of Nostradamus 2013: International Conference Prediction, Modeling and Analysis of Complex Systems, Springer Series: Advances in Intelligent Systems and Computing, vol. 210, pp. 467–474 (2013)
Zelinka, I., Davendra, D., Senkerik, R., Jasek, R., Oplatkova, Z: (2011). Analytical programming—a novel approach for evolutionary synthesis of symbolic structures. In: Kita, E. (ed.) Evolutionary Algorithms. ISBN: 978-953-307-171-8, InTech, doi:10.5772/16166. http://www.intechopen.com/books/evolutionary-algorithms/analytical-programming-a-novel-approach-for-evolutionary-synthesis-of-symbolic-structures
Koza, J.: Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Stanford University, Computer Science Department, Technical Report, STAN-CS-90-1314 (1990)
Koza, J.: Genetic Programming. MIT Press, Cambridge (1998)
O’Neill, M., Ryan, C.: Grammatical Evolution, Evolutionary Automatic Programming in an Arbitrary Language. Springer, New York (2003)
Ryan, C., Collins, J., O’Neill, M.: Grammatical Evolution: Evolving Programs for an Arbitrary Language. Lecture Notes in Computer Science, First European Workshop on Genetic Programming (1998)
Zelinka, I., Oplatkova, Z., Nolle, L.: Analytic programming—symbolic regression by means of arbitrary evolutionary algorithms. Int. J. Simul. Syst. Sci. Technol. 6(9), 44–56 (2005)
Johnson, C.: Artificial immune systems programming for symbolic regression. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poliand, R., Costa, E. (eds.) Lecture Notes in Computer Science, pp. 345–353. Springer, Berlin (2004)
Weisser, R., Osmera, P.: Two-level transplant evolution for optimization of general controllers. In: New Trends in Technologies. Sciyo, Croatia (2010)
Weisser, R., Osmera, P.: Two-level tranpslant evolution. In: 17th Zittau Fuzzy Colloquium, Zittau, Germany (2010)
Weisser, R., Osmera, P., Matousek, R.: Transplant evolution with modified schema of differential evolution: optimization structure of controllers. In: International Conference on Soft Computing MENDEL, Brno, Czech Republic (2010)
O’Neill, M., Brabazon, A.: Grammatical differential evolution. In: Proceedings of International Conference on Artificial Intelligence, pp. 231–236. CSEA Press (2006)
Koza, J., Bennet, F., Andre, D., Keane, M.: Genetic Programming III. Morgan Kaufmann, New York (1999)
Zelinka, I., Oplatkova, Z.: Analytic programming—comparative study. In: Proceedings of Second International Conference on Computational Intelligence, Robotics, and Autonomous Systems, Singapore (2003)
Koza, J., Keane, M., Streeter, M.: Evolving inventions. Sci. Am. 40–47 (2003)
Oplatkova, Z., Zelinka, I.: Investigation on artificial ant using analytic programming. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 949–950, Seattle, WA (2006)
Zelinka, I., Senkerik, R., Pluhacek, M.: Do evolutionary algorithms indeed require randomness? In: IEEE Congress on Evolutionary Computation, pp. 2283–2289. Cancun, Mexico (2013)
Zelinka, I., Chadli, M., Davendra, D., Senkerik, R., Pluhacek, M., Lampinen, J.: Hidden periodicity—chaos dependance on numerical precision. In: Proceedings of Nostradamus 2013: International Conference Prediction, Modeling and Analysis of Complex Systems, Springer Series: Advances in Intelligent Systems and Computing, vol. 210, pp. 47–59 (2013)
Zelinka, I., Chadli, M., Davendra, D., Senkerik, R., Pluhacek, M., Lampinen, J.: Do evolutionary algorithms indeed require random numbers? extended study. In: Proceedings of Nostradamus 2013: International Conference Prediction, Modeling and Analysis of Complex Systems, Springer Series: Advances in Intelligent Systems and Computing, vol. 210, pp. 61–75 (2013)
Zelinka I.: SOMA—self organizing migrating algorithm. In: Babu, B.V., Onwubolu, G., (eds.) New Optimization Techniques in Engineering, pp. 167–218. Springer, New York (2004)
Price K.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F., (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill, London (1999)
Zelinka, I., Chen, G., Celikovsky, S.: Chaos Synthesis by means of evolutionary algorithms. Int. J. Bifurcat. Chaos 18(4), 911–942 (2008). ISSN 0218–1274
Zelinka, I., Chen, G., Celikovsky, S.: Evolutionary Algorithms and Chaotic Systems. Springer, Germany (2010)
Acknowledgments
The following two grants are acknowledged for the financial support provided for this research: Grant Agency of the Czech Republic—GACR P103/13/08195S, by the Development of human resources in research and development of latest soft computing methods and their application in practice project, reg. no. CZ.1.07/2.3.00/20.0072 funded by Operational Programme Education for Competitiveness, co-financed by ESF and state budget of the Czech Republic, partially supported by Grant of SGS No. SP2013/114, VŠB—Technical University of Ostrava, Czech Republic, and by European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089.
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Zelinka, I., Skanderova, L., Saloun, P., Senkerik, R., Pluhacek, M. (2014). Chaos Powered Symbolic Regression in Be Stars Spectra Modeling. In: Sanayei, A., Zelinka, I., Rössler, O. (eds) ISCS 2013: Interdisciplinary Symposium on Complex Systems. Emergence, Complexity and Computation, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45438-7_13
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