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A new optimization algorithn based on a paradigm inspired by nature

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International Seminar on Computational Intelligence held at Tijuana, Mexico on January of 2010
Języki publikacji
EN
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EN
In this paper, we propose a new optimization algorithm for soft computing problems, which is inspired on a nature paradigm: the reaction methods existing on chemistry, and the way the elements combine with each other to form compounds, in other words, quantum chemistry. This paper is the first approach for the proposed method, and it presents the background, main ideas, desired goals and preliminary results in optimization.
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Bibliografia
  • [1] Hidalgo D., Melin P., Licea, G., “Optimization of Modular Neural Networks with Interval Type-2 Fuzzy Logic Integration Using an Evolutionary Method with Application to Multimodal Biometry”, Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition, 2009, pp. 111-121.
  • [2] AstudilloL.,CastilloO.,AguilarL.,“HybridControlfor an Autonomous Wheeled Mobile Robot Under Perturbed Torques”, Foundations of Fuzzy Logic and Soft Computing, IFSA Proceedings , 2007, pp. 594-603.
  • [3] Melin P., Mancilla A., Lopez M., Solano D., Soto M., Castillo O., “Pattern Recognition for Industrial Security Using the Fuzzy Sugeno Integral and Modular Neural Networks”, Advances in Soft Computing , vol. 39, no. 1, 2007, pp. 105-114.
  • [4] Kennedy J., Eberhart R. C., “Particle swarm optimization”, Proceedings of IEEE International Conference on Neural Networks , Piscataway, NJ, 1995, pp. 19421948.
  • [5] Valdez F., Melin P., “Parallel Evolutionary Computing using a cluster for Mathematical Function Optimization“, Nafips. San Diego CA, USA, June 2007, pp. 598602.
  • [6] Fogel D.B., “An introduction to simulated evolutionary optimization”, IEEE transactions on neural networks , vol. 5, no. 1, 1994, pp. 3-14.
  • [7] Man K.F., Tang K.S., Kwong S., "Genetic Algorithms: Concepts and Designs", Springer Verlag , 1999.
  • [8] Eberhart R. C., Kennedy J., “A new optimizer using particle swarm theory”. In: Proceedings of the 6 th InternationalSymposium on Micromachine and Human Science, Nagoya, Japan, 1995, pp. 39-43.
  • [9] Goldberg D., Genetic Algorithms, Addison Wesley, 1988.
  • [10] Angeline P. J., “Using Selection to Improve Particle Swarm Optimization”. In: Proceedings of the World Congress on Computational Intelligence , Anchorage, Alaska, IEEE, 1998, pp. 84-89.
  • [11] Montiel O., Castillo O., Melin P., Rodriguez, A., Sepulveda, R. “Human evolutionary model: A new approach to optimization”, Inf. Sci , no. 177(10), 2007, pp. 20752098.
  • [12] Haupt R.L., Haupt S.E., “Practical Genetic Algorithms”, 2 nd Edition, Wiley Interscience, 2004.
  • [13] Rotar C., “A New Evolutionary Algorithm for Multiobjective Optimization Based on the Endocrine System”. In: Proceedings of the International Conference on Theory and Applications of Mathematics and Informatics ICTAMI ,Alba Iulia, 2003.
  • [14] Chang R., “General Chemistry ”, 5 th edition, McGraw-Hill, 2004.
  • [15] Goldberg D., “ Schaum's Outline of Beginning Chemistry”, 3 rd edition, Schaum's Outline Series, McGraw-Hill, 2009.
  • [16] GEATbx: Example Functions (single and multi-objective functions), http://www.geatbx.com/docu/fcnindex01.html#P89_3085
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Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ5-0030-0038
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