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Citations by Other Authors:

  1. J. Nenortaite and R. Simutis.
    Adapting Particle Swarm Optimization to Stock Markets,
    In Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), pp. 520-525, 2005.

    Cites C13

  2. D. Palmer-Brown and M. Kang.
    ADFUNN: An adaptive function neural network,
    In Adaptive and Natural Computing Algorithms, Ribeiro, B.; Albrecht, R. F.; Dobnikar, A.; Pearson, D. W.; Steele, N. C. (Eds.)
    Springer-Verlag New York, LLC, May 3, pp. 1-4, 2005.

    Cites C7.

  3. Hui Yu, Jian Chen and Caihong Sun.
    N-Person Noncooperative Game with Infinite Strategic Space,
    In Lecture Notes in Computer Science,
    Springer-Verlag GmbH, 3521: 77-84, 2005.

    Cites J7.

  4. G. Demir and B. Yener.
    Automated cancer diagnosis based on histopathological images: a systematic survey,
    Technical Report TR-05-09, Computer Science Department at Rensselaer Polytechnic Institute, Troy NY, U.S., 2005.

    Cites E2.

  5. R. Farjam, H. Soltanian-Zadeh, R. A. Zoroofi and K. Jafari-Khouzani.
    Tree-structured grading of pathological images of prostate,
    In Progress in Biomedical Optics and Imaging, Proceedings of SPIE, 5747:840-851, 2005.

    Cites E2.

  6. D. H. Mantzaris, G. C. Anastassopoulos and A. V. Adamopoulos.
    Intelligent prediction of vesicoureteral reflux disease,
    In WSEAS Transactions on Systems, 4(9): 1440-1449, 2005.

    Cites E2.

  7. D. H. Mantzaris, G. C. Anastassopoulos, A. D. Tsakalidis and A. V. Adamopoulos.
    Vesicoureteral Reflux Prognosis Using Artificial Neural Networks,
    In Proceedings of the Fifth WSEAS International Conference on Simulation, Modeling and Optimization, pp. 439-444, 2005.

    Cites E2.

  8. B. Liu, L. Wang, Y. H. Jin and D. X. Huang.
    Advances in particle swarm optimization algorithm,
    Control and Instruments in Chemical Industry, 32(3):1-7, 2005.

    Cites J7.

  9. J. Nenortaite.
    Computation improvement of stock market decision making model through the Application of GRID,
    Information Technology and Control, 34(3):269-275, 2005.

    Cites C13.

  10. K. Zielinski, D. Peters and R. Laur.
    Constrained Multi-Objective Optimization Using Differential Evolution,
    In Proceedings of the Third International Conference on Computational Intelligence, Robotics and Autonomous Systems, (CIRAS 2005), 2005.

    Cites C9.

  11. S. Kukkonen and J. Lampinen.
    GDE3: The third evolution step of generalized differential evolution,
    In Proceedings of the IEEE Congress on Evolutionary Computation, (CEC 2005), 1:443-450, 2005.

    Cites C9.

  12. E. Alba, E-G. Talbi, G. Luque and N. Melab.
    Metaheuristics and Parallelism,
    In Parellel Metaheuristics: A New Class of Algorithms, E. Alba (ed.), Chapter 4, pp. 79-104, Wiley Series on Parallel and Distributed Computing, John Wiley & Sons Inc, Hoboken, New Jersey, USA, October 2005, ISBN 0-471-67806-6.

    Cites C9

  13. A. J. Nebro, F. Luna, E-G. Talbi and E. Alba.
    Parallel Multiobjective Optimization,
    In Parellel Metaheuristics: A New Class of Algorithms, E. Alba (ed.), Chapter 16, pp. 371-394, Wiley Series on Parallel and Distributed Computing, John Wiley & Sons Inc, Hoboken, New Jersey, USA, October 2005, ISBN 0-471-67806-6.

    Cites C9

  14. C. Gunduz, B. Yener and S. H. Gultekin.
    The cell graphs of cancer,
    Bioinformatics, 20: 1145-1151, 2004.

    Cites E2.

  15. C. Demity and B. Yener.
    Automated cancer diagnosis based on histopathological images: a systematic survey,
    Technical Report TR-05-09, Rensselaer Polytechnic Institute, Department of Computer Science, 2004.

    Cites E2.

  16. Maiga Chang and Ko-Kang Chu.
    A hybrid training mechanism for applying neural networks to web-based applications,
    In IEEE International Conference on Systems, Man and Cybernetics, (SMC 2004), pp. 3543-3547, The Hague, The Netherlands, 2004.

    Cites E2.

  17. J. Nenortaite and R. Simutis.
    Stocks' trading system based on the particle swarm optimization algorithm,
    In Lecture Notes in Computer Science, 3039: 843-850. Springer-Verlag, 2004.

    Cites C13.

  18. G. Demir and S. H. Gultekin.
    Learning the topological properties of brain tumors,
    Technical Report TR-04-14, Computer Science Department at Rensselaer Polytechnic Institute, Troy NY, U.S., 2003.

    Cites E2.

  19. M. R. Tir.
    Data mining en assurance: Quelques utilisations,
    In $IV$ Forum des Assurances d'Alger Réformes dans les assurances : plus de rigueur au service de la société , November 28-29, 2005, Alger.

    Cites C13.

  20. M. R. Tir.
    A propos de l'utilisation de l'intelligence artificielle en finance: aperçu de quelques techniques,
    In Proceedings of Le Système National d'Information Economique : Etat et Perspectives, January 31-Febrouary 1, 2005, Ben Aknoun, Alger.

    Cites C13.

  21. Cecilia Di Chio.
    Extended Particle Swarms to Simulate Biology-Like Systems,
    Full research proposal, Department of Computer Science, University of Essex, U.K. Supervisor Prof Riccardo Poli, July 4, 2005.

    Cites J7.

  22. R. Angira and B. V. Babu.
    Optimization of non-linear chemical processes using modified differential evolution,
    In Proceedings 2nd Indian International Conference on Artificial Intelligence (IICAI-05), December 20-22, Pune, Maharashtra, India, Bhanu Prasad (Ed.) pp. 911-923, 2005 .

    Cites C10.

  23. D. H. M. Nguyen, K. P. Wong and M. Ilic.
    Determining the Nash Equilibrium of ``Black-box'' Electricity Markets,
    IEEE Transactions on Power Systems, in press.

    Cites J7.

  24. M. Mekhilef.
    Twinkling a random search algorithm for design optimization,
    In Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC2005), Long Beach, California, USA, 2(A):321-330, 2005.

    Cites J7.

  25. K. P. Wong and Z. Y. Dong.
    Differential evolution, an alternative approach to evolutionary algorithm,
    In Proceedings of the Thirteenth International Conference on Intelligent Systems Application to Power Systems, (ISAP 2005), Arlington, U.S.A., pp. 73-83, 2005.

    Cites C10.

  26. A. P. Engelbrecht.
    Fundamentals of Computational Swarm Intelligence,
    Wiley, November, 2005.

    Cites C10.

  27. B. V. Babu and R. Angira.
    Modified differential evolution (MDE) for optimization of non-linear chemical processes,
    Computers and Chemical Engineering, 30(6-7):989-1002, 2006.

    Cites C10.

  28. G. Demir, S. H. Gultekin and B. Yener.
    Learning the topological properties of brain tumors,
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2(3):262-270, 2005.

    Cites E2.

  29. A. Kasinski and F. Ponulak.
    Comparison of supervised learning methods for spike time coding in spiking neural networks,
    International Journal of Applied Mathematics and Computer Science, 16(1):101-113, 2006.

    Cites C7.

  30. C. Rocha-Alicano, D. Covarrubias-Rosales, C. Brizuela-Rodriguez and M. Panduro-Mendoza.
    Differential evolution algorithm applied to sidelobe level reduction on a planar array,
    AEÜ International Journal of Electronics and Communications, (in press).

    Cites C10.

  31. V. L. Huang, P. N. Suganthan and S. Baskar.
    Multiobjective Differential Evolution with External Archive,
    Technical Report MODE-2005,School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 2005.

    Cites C9.

  32. Q. Yu and X. J. Wang.
    Evolutionary Algorithm for Solving Nash Equilibrium Based on Particle Swarm Optimization,
    Journal of Wuhan University (Natural Science Edition), 52(1):25-29, 2006.

    Cites J7.

  33. A. G. Hernandez-Diaz, L. V. Santana-Quintero, C. A. Coello Coello, R. Caballero and J. Molina.
    A new proposal for multi-objective optimization using differential evolution and rough sets theory,
    In Proceedings of the Genetic and Evolutionary Computation Conference, (GECCO 2006), Seattle, Washington, USA, pp. 675-682, July 2006.

    Cites C10.

  34. H. Zhang and P. Liu.
    A Momentum-Based Approach to Learning Nash Equilibria,
    In Lecture Notes in Computer Science, Z.Shi and R.Sadananda (Eds.), Springer Berlin / Heidelberg, 4088:528-533, 2006.

    Cites J7.

  35. F. Luna, A. J. Nebro and E. Alba.
    Observations in using Grid-enabled technologies for solving multi-objective optimization problems,
    Parallel Computing, 32(5-6):377-393, 2006.

    Cites C9.

  36. E. Ozcan.
    An Empirical Investigation on Memes, Self-generation and Nurse Rostering,
    In The 6th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2006), E. K. Burke, H. Rudová (Eds.), Masaryk University, Brno, Czech Republic, 2006.

    Cites C10.

  37. L. V. Santana-Quintero and C. A. Coello Coello.
    An algorithm based on differential evolution for multiobjective problems,
    In Smart Engineering System Design: Neural Networks, Evolutionary Programming and Artificial Life, Cihan H. Dagli, Anna L. Buczak, David L. Enke, Mark J. Embrechts and Okan Ersoy (editors), 15:211-220, ASME Press, St. Louis, Missouri, USA, November 2005.

    Cites C9.

  38. J. Nenortaite and R. Simutis.
    Development and evaluation of decision-making model for stock markets,
    Journal of Global Optimization, 36(1):1-19, 2006.

    Cites C13.

  39. X.-S. He, B. Yu and L. Han.
    A BP neural network learning algorithm based on differential evolution,
    Fangzhi Gaoxiao Jichukexue Xuebao, 19(2):178-181, 2006.

    Cites C9.

  40. F. Luna, A. J. Nebro and E. Alba.
    Parallel evolutionary multiobjective optimization,
    Studies in Computational Intelligence, 22:33-56, 2006.

    Cites C9.

  41. K. N. Kozlov and A. M. Samsonov.
    New migration scheme for parallel differential evolution,
    In Proceedings of the 5th International Conference on Bioinformatics of Genome Regulation and Structure (BGRS 2006), July 16-22, 2006, Novosibirsk, Russia, Institute of Cytology and Genetics, Russian Academy of Sciences Siberian Branch, N. Kolchanov, R. Hofestädt (Eds.), 2:141-144, Novosibirsk 2006.

    Cites C10.

  42. B. V. Babu and R. Angira.
    Performance of modified differential evolution for optimal design of complex and non-linear chemical processes,
    Journal of Experimental and Theoretical Artificial Intelligence, 18(4):501-512, 2006.

    Cites C10.

  43. M. Assaad, R. Bone and H. Cardot.
    A new boosting algorithm for improved time-series forecasting with recurrent neural networks,
    Information Fusion, (2006), doi:10.1016/j.inffus.2006.10.009.

    Cites C6.

  44. C. F. Hong, Y. S. Liao, M. H. Lin and T. H. Hong.
    A Study of Improving the Coherence in Multi-Step Ahead Forecasting,
    In Advances in Intelligent Systems Research, Proceedings of the 9th Joint Conference on Information Sciences, eds. H. D. Cheng, S. D. Chen and R. Y. Lin, the Atlantis Press, pp. 230-234, October 2006.

    Cites C13 and C6.

  45. C. Rocha-Alicano, D. Covarrubias-Rosales and C. Brizuela-Rodriguez.
    Performance evaluation of two array factor synthesis techniques for steerable linear arrays,
    In Proceedings of the Third IASTED International Conference on Antennas, Radar and Wave Propagation, pp. 103-108, 2006.

    Cites C9.

  46. Q. Zhang, C. Zhou, W. Xiao, P. C. Nelson and X. Li.
    Using Differential Evolution for GEP Constant Creation,
    In Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006), 2006.

    Cites C9.

  47. P. Forsberg.
    Optimisation of Long-Term Industrial Planning,
    PhD Thesis, Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, 2006.

    Cites C6.

  48. C. C. Bilgin, C. Demir, C. Nagi and B. Yener B.
    Cell-Graph Mining for Breast Tissue Modelling and Classification,
    Technical Report 07-02, Computer Science Department at Rensselaer Polytechnic Institute, Troy NY, U.S., 2007.

    Cites E2.

  49. C. L. Müller, G. Paul and I. F. Sbalzarini.
    Sensitivities for free: CMA-ES based sensitivity analysis,
    In Fifth International Conference on Sensitivity Analysis of Model Output, (SAMO 2007), June 18-22, Eötvös University (ELTE), Budapest, Hungary, 2007.

    Cites J7.

  50. J. Ko\lodzief, K. Jauernig and A. Cieslar.
    HGSNash Strategy as the Decision-Making Method for Water Resource Systems with External Disagreement of Interests,
    In Proceedings of the International Symposium on Parallel Computing in Electrical Engineering (PARELEC'06), 2006.

    Cites J7.

  51. L. Singh and S. Kumar.
    Parallel Evolutionary Asymmetric Subsethood Product Fuzzy-Neural Inference System: An Island Model Approach,
    In Proceedings of the IEEE International Conference on Computing: Theory and Applications (ICCTA 2007), Platinum Jubilee Conference, March 5-7, Kolkata, India, pp. 282-286, 2007.

    Cites C10.

  52. M. J. Reddy and D. N. Kumar.
    Multiobjective Differential Evolution with Application to Reservoir System Optimization,
    Journal of Computing in Civil Engineering, 21(2): 136-146, 2007.

    Cites C9.

  53. C. Gunduz-Demir.
    Mathematical modeling of the malignancy of cancer using graph evolution,
    Mathematical Biosciences, in press.

    Cites E2.

  54. W.-B. Liu and X.-J. Wang.
    An evolutionary game based particle swarm optimization algorithm,
    Journal of Computational and Applied Mathematics, in press.
    Cites J7.


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Next: About this document ... Up: Curriculum Vitae Previous: International Refereed Conferences:
2007-05-29