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Hybridisation of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimisation Problems

References Gosavi A. Simulation Optimisation: Parametric Optimisation Techniques and Reinforcement Learning. - Norvell: Kluwer Academic Publishers, 2003, 551 p. Deb K. Evolutionary algorithms for multi-criterion optimisation in engineering design. Proceedings of Evolutionary Algorithms in Engineering and Computer Science. - London: John Wiley and Sons, Ltd., 1999. 135-161 p. Jourdan L., Basseur M., Talbi E.G. Hybridizing exact methods and metaheuristics: A taxonomy. European

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Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of Distributed Evolutionary Algorithms

References 1. Goldberg, D. E. Genetic and Evolutionary Algorithms Come of Age. – Communications of the ACM, Vol. 37 , 1994, No 3, pp. 113-119. 2. Pappa, G., G. Ochoa, M. Hyde, A. Freitas, J. Woodward, J. Swan. Contrasting Meta-Learning and Hyper-Heuristic Research: The Role of Evolutionary Algorithms. – Genetic Programming and Evolvable Machines, Vol. 15 , 2014, Issue 1, pp. 3-35. 3. Adamidis, P. Review of Parallel Genetic Algorithms Bibliography. Tech. Rep. Version 1. Aristotle University of Thessaloniki, Thessaloniki, Greece, 1994. 4

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Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2:221-248. Stewart, T., andWest, R. 2010. Testing for equivalence: A methodology for computational cognitive modelling. Journal of Artificial General Intelligence 2:69-87. Tor, K., and Ritter, F. E. 2004. Using a Genetic Algorithm to Optimize the Fit of Cognitive Models. In Proceedings of the Sixth International Conference on Cognitive Modeling, 308-313. Mahwah, NJ: Lawrence Erlbaum.

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Evolutionary Algorithm for Calculating Available Transfer Capability

Restoration Energy in Distribution Systems, Journal of Electrical Engineering 63 No. 1 (2012), 47-52. [13] ELMAOUHAB, A.-BOUDOUR, M.-GUEDDOUCHE, R. : New Evolutionary Technique for Optimization Shunt Capacitors in Distribution Networks, Journal of Electrical Engineering 62 No. 3 (2011), 163-167. [14] PERUMAL, M. P.-NANJUDAPAN, D. : Performance Enhancement of Embedded System BasedMultilevel Inverter Using Genetic Algorithm, Journal of Electrical Engineering 62 No. 4 (2011), 190-198. [15] ABOURA, S.-OMARI, A.-MEGUENNI, K. Z

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Solution of Linear and Non-Linear Boundary Value Problems Using Population-Distributed Parallel Differential Evolution

References [1] Gong, Y.J., Chen, W.N., Zhan, Z.H., Zhang, J., Li, Y., Zhang, Q. and Li, J.J., 2015, Distributed evolutionary algorithms and their models: A survey of the state-of-the-art, Applied Soft Computing, 34, pp. 286-300. DOI: 10.1016/j.asoc.2015.04.061 [2] Zelinka, I., 2015, A survey on evolutionary algorithms dynamics and its complexity–Mutual relations, past, present and future, Swarm and Evolutionary Computation, 25, pp. 2-14. DOI: 10.1016/j.swevo.2015.06.002 [3] Price, K., Storn, R.M. and Lampinen, J.A., 2006, Differential evolution

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Effect of Strategy Adaptation on Differential Evolution in Presence and Absence of Parameter Adaptation: An Investigation

References [1] A. E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 3 (2), 124–141, 1999. [2] G. Beni, J. Wang, Swarm Intelligence in Cellular Robotic Systems, in: Proceedings of the NATO Advanced Workshop on Robots and Biological Systems. Tuscany, Italy, 1989. [3] P.J. Angeline, Adaptive and self-adaptive evolutionary computation, in: M. Palaniswami, Y. Attikiouzel, R.J. Marks, D.B. Fogel, T. Fukuda (Eds.), Computational Intelligence: A Dynamic System

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References Adamczewski, Z. (1992). Krzywa łańcuchowa jako linia realna. Przegląd Geodezyjny nr 4 . Warszawa. Arabas, J. (2001). Wykłady z algorytmów ewolucyjnych . Warszawa: Wydawnictwo Naukowo - Techniczne. Civivioglu P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm, Computers and Geosciences, Volume 46, pp. 229-247 . Goldberg, D. E. (2003). Algorytmy genetyczne i ich zastosowanie . Warszawa: Wydawnictwo Naukowo

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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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Spectrum Allocation of Cognitive Radio Based on Autonomy Evolutionary Algorithm

Access. - In: Proc. of IEEE International Conference on Communications, Vol. 5, 2005, pp. 3132-3136. 13. Anumandla, K. K., S. Kudikala, B. A. Venkata, S. L. Sabat. Spectrum Allocation in Cognitive Radio Networks Using Firefly Algorithm. - Swarm, Evolutionary, and Memetic Computing, Vol. 8297, 2013, pp. 366-376. 14. Li, X. B., L. Lui, A. W. Shi, M. A. Kai, X. P. Guan. Cognitive Radio Spectrum Allocation Based on an Improved Population Adaptive Artificial Bee Colony Algorithm. - Journal of Applied Sciences, Vol. 31, 2013, No 5, pp. 448

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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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