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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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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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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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The performance profile: A multi–criteria performance evaluation method for test–based problems

escalation in a co-evolutionary arms race, International Journal of Knowledge-Based Intelligent Engineering Systems 4 (3): 191–200. de Jong, E.D. (2004). The incremental Pareto-coevolution archive, in K. Deb et al. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference , Lecture Notes in Computer Science, Vol. 3102, Springer-Verlag, Berlin/Heidelberg, pp. 525–536. Ficici, S.G. (2004). Solution Concepts in Coevolutionary Algorithms , Ph.D. thesis, Brandeis University, Waltham, MA. Fogel, D.B. (1991). The evolution of intelligent

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Self-adaptation of parameters in a learning classifier system ensemble machine

. (Eds) (2008). Learning Classifier Systems in Data Mining , Springer, Berlin/Heidelberg. Bull, L., Studley, M., Bagnall, A. and Whittley, I. (2007). Learning classifier system ensembles with rulesharing, IEEE Transactions on Evolutionary Computation   11 (4): 496-502. Butz, M. V. (1999). An implementation of the XCS classifier system in C, Technical Report 99021 , Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana-Champaign, IL. Butz, M. V., Sastry, K., Goldberg, D. E

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Evolutionary optimization of interval mathematics-based design of a TSK fuzzy controller for anti-sway crane control

genetic algorithm approach to controller design, Engineering Applications of Artificial Intelligence 12(4): 401-409. Hsu, C.-C., Chang, S.-C. and Yu, C.-Y. (2007). Tolerance design of robust controllers for uncertain interval systems based on evolutionary algorithms, IET Control Theory and Applications 1(1): 244-252. Hyla, P. (2012). The crane control systems: A survey, Proceedings of the 17th IFAC International Conference on Methods and Models in Automation and Robotics MMAR, Mi˛edzyzdroje, Poland, pp. 505-509. Kang, Z

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