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Approximation of phenol concentration using novel hybrid computational intelligence methods

References Antonelli, M., Ducange, P., Lazzerini, B. and Marcelloni, F. (2009). Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework, International Journal of Approximate Reasoning 50 (7): 1066-1080. Aydogan, E., Karaoglan, I. and Pardalos, P. (2012). hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems, Applied Soft Computing 12 (2): 800-806. Benrekia, F., Attari, M. and Bermak, A

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Evolutionary algorithms and fuzzy sets for discovering temporal rules

11th International Conference on Data Engineering, Taipei, Taiwan, pp. 3-14. Alcalá, R., Alcal´a-Fdez, J., Gacto, M. and Herrera, F. (2007a). A multi-objective evolutionary algorithm for rule selection and tuning on fuzzy rule-based systems, Proceedings of the IEEE International Fuzzy Systems Conference (FUZZIEEE 2007), London, UK, pp. 1-6. Alcalá, R., Alcal´a-Fdez, J. and Herrera, F. (2007b). A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection, IEEE

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The island model as a Markov dynamic system

References Alba, E. and Tomassini, M. (2002). Parallelism and evolutionary algorithms, IEEE Transactions on Evolutionary Computation 6 (5): 443-462. Aparicio, J., Correia, L. and Moura-Pires, F. (1999). Populations are multisets-plato, in W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela and R.E. Smith (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, 13-17 July 1999 , Vol. 2, Morgan Kaufmann, San Francisco, CA, pp. 1845-1850. Bäck

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Stability Analysis and its Impact on the Parameters Estimation for a Logistic Growth Model

, A. Perelson, M. Fridkis-Hareli, and A. Globerson, Feedback regulation of T cell development: manifestations in aging, Mechanisms of Ageing and Development, 91, (1996), 195-210 [5] C.L. Ortman, K.A. Dittmar, P.L. Witte, and P.T. Le, Molecular characterization of the mouse involuted thymus: aberrations in expression of transcription regulators in thymocyte and epithelial compartments, International Immunology, 14 (7), (2002), 813-822 [6] P. Posik and V. Klema, JADE, an adaptive differential evolution algorithm, benchmarked on the BBOB

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A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

Nonlinear Systems , Springer, Berlin/Heidelberg. Shill, P., Akhand, M. and Murase, K. (2011). Simultaneous design of membership functions and rule sets for type-2 fuzzy controllers using genetic algorithms, 14th International Conference on Computer and Information Technology, Dhaka, Bangladesh , pp. 554–559. Shukla, P. and Tripathi, S. (2013). Interpretability issues in evolutionary multi-objective fuzzy knowledge base systems, 7th International Conference on Bio-Inspired Computing: Theories and Applications, Madhya Pradesh, India , pp. 473

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Heuristic algorithms for optimization of task allocation and result distribution in peer-to-peer computing systems

References Anderson, D.P. (2004). BOINC: A system for public-resource computing and storage, 5th IEEE/ACM International Workshop on Grid Computing, Pittsburgh, PA, USA , pp. 4-10. Arthur, D. and Panigrahy, R. (2006). Analyzing BitTorrent and related peer-to-peer networks, Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithm, SODA’06, ACM, New York, NY, pp. 961-969, DOI: 10.1145/1109557.1109664. BOINC (2011). BOINC poject, http://boinc.berkeley.edu/. Chmaj, G. and Walkowiak, K. (2008

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A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization

References Adcock, S. (n.d.). Genetic algorithm utility library, http://gaul.sourceforge.net/ Angeline, P. J. (1995). Adaptive and self-adaptive evolutionary computations, in M. Palaniswami, Y. Attikiouzel, R. Marks, D. Fogel and T. Fukuda (Eds.) Computational Intelligence: A Dynamic Systems Perspective , IEEE Press, Ann Arbor, MN, p. 152. BéaUck, T. (1993). Optimal mutation rates in genetic search, in S. Forrest (Ed.), Proceedings of the 5th International Conference on

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Nonlinear Feature Extraction in a Logarithmic Space with Evolutionary Algorithms

References [1] C. M. Fonseca, E. M. Mendes, P. J. Fleming, and S. A. Billings, Non-linear Model Term Selection with Genetic Algorithms, 1993. [2] J. H. Friedman and J. W. Tukey, A projection pursuit algorithm for exploratory data analysis, IEEE Trans. Comput., C23(9), (1974), 881890. [3] J.H. Friedman, Exploratory Projection Pursuit, American Statistical Association, (1987), 249266. [4] J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975

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Stochastic Fractal Based Multiobjective Fruit Fly Optimization

References Aguirre, A.H., Rionda, S.B., Coello Coello, C.A., Lizárraga, G.L. and Montes, E.M. (2004). Handling constraints using multiobjective optimization concepts, International Journal for Numerical Methods in Engineering 59(15): 1989-2017. Alcalá-Fdez, J., Sanchez, L., Garcia, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J. and Rivas, V.M. (2009). KEEL: A software tool to assess evolutionary algorithms for data mining problems, Soft Computing 13(3): 307-318. Asafuddoula

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Evolutionary Computation in Cryptanalysis of Classical Ciphers

Technology, 1998. [5] GROŠSEK, O.-VOJVODA, M.-ZAJAC, P.: Classical Ciphers. STU Bratislava, 2007. (In Slovak) [6] GROŠEK, O.-ZAJAC, P.: Automated cryptanalysis of classical ciphers. In: Encyclopedia of Artificial Intelligence, 2008, pp. 186-191. [7] Automated cryptanalysis. In: Encyclopedia of Artificial Intelligence, 2009, pp. 179-185. [8] SEKAJ, I.: Evolutionary Computations and their Usage in Practice Iris, 2005. (In Slovak) [9] SEKAJ, I.-ORAVEC, M.: Parallel evolutionary algorithms. In

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