Search Results

1 - 10 of 23 items :

  • evolutionary algorithms x
  • Artificial Intelligence x
  • Software Development x
Clear All
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

Open access
Agent-Based Evolutionary Method of Simulation the Co2 Emission Permits Market

References [1] Alkemade F., La Poutre H., Amman H. M. Robust Evolutionary Algorithm Design for Socio-economic Simulation, Computational Economics 28 , 2006, pp. 355-470. [2] Bartoszczuk P., Horabik J. Tradable Permit Systems: Considering Uncertainty in Emission Estimates, Water Air Soil Pollution: Focus (2007) 7 , , Springer Verlag, 2007, pp 573-579. [3] Bonatti M., Ermoliev Y., Gaivoronski A. Modeling of multi-agent market systems in the presence of uncertainty: The case of information economy

Open access
Handling the Multiplicity of Solutions in a Moea Based PDA-THESEUS Framework for Multi-Criteria Sorting

References [1] Butler, J., Jia, J., Dyer, J. (1997): Simulation techniques for the sensitivity analysis of multi-criteria decision models, European Journal of Operational Research 103, 3, 531-546. [2] Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A. (2007): Evolutionary Algorithms for Solving Multi_Objective Problems, Second Edition. Springer, New York. [3] Corrente, S., Doumpos, M., Greco, S., Slowinski, R., Zopounidis, C. (2015): Multiple criteria hierarchy process for sorting problems based on ordinal

Open access
Evolutionary multi-objective optimization for inferring outranking model’s parameters under scarce reference information and effects of reinforced preference

References [1] Butler, J., Jia, J., Dyer, J., Simulation techniques for the sensitivity analysis of multi-criteria decision models, European Journal of Operational Research 103, 1997, 531-546. [2] Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A., Evolutionary Algorithms for SolvingMultiObjective Problems, Second Edition. Springer, New York, 2007. [3] Deb, K., Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, Chichester-New York-Weinheim-Brisbane-Singapore-Toronto, 2001

Open access
Primal–Dual Type Evolutionary Multiobjective Optimization

References Coello Coello C. A., Van Veldhuizen D.A., and Lamont G.B. (2002), Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York. Deb K. (2001), Multi-objective Optimization Using Evolutionary Algorithms. JohnWiley and Sons, Chichester. Hanne T. (2007), A multiobjective evolutionary algorithm for approximating the efficient set. European Journal of Operational Research, 176, 1723-1734. Kaliszewski I., Miroforidis J. (2012), Real and virtual

Open access
Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

. [6] Clark D. M. Evolution of algebraic terms 1: term to term operation continuity. International Journal of Algebra and Computation, 23(05):1175-1205, 2013. [7] Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, April 2002. [8] Ficici S. G. and Pollack J. B. Pareto optimality in coevolutionary learning. In Kelemen J. and Sosík P., editors, Advances in Artificial Life, 6th European Conference, ECAL 2001, volume 2159

Open access
Benchmarking of Problems and Solvers: a Game-Theoretic Approach

References [1] Auger, A., Hansen, N., Performance evaluation of an advanced local search evolutionary algorithm, in: Proceedings of the IEEE Congress on Evolutionary Computation, 2, 2005, 1777-1784. [2] Benson, H.Y., Shanno, D.F., Vanderbei, R.J., Interior-point methods for nonconvex nonlinear programming: Jamming and comparative numerical testing, Operations Research and Financial Engineering, Princeton University, Technical Report ORFE-00-02, 2000. [3] Billups, S.C., Dirkse, S.P., Ferris, M.C., A comparison of algorithms for large-scale mixed

Open access
Genetic Algorithm Modification for Production Scheduling

, Springer, Berlin 2008. [5] Gao J., Gen M., Sun L. A hybrid of genetic algorithm and bottleneck shifting for flexible job shop scheduling problem, GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation , ACM 2006, 1158. [6] Goldberg D.E., Genetic Algorithms in Search, Optimization, and Machine Learning , Dorling Kindersley Pvt Ltd, New Delhi 2008, 17. [7] Grimes D., Hebrard E., Model and strategies for variants of the job shop scheduling problem, CP'11: Proceedings of the 17th

Open access
An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Algorithm using Cuckoo Search and Differential Evolution for Data Clustering. Indian Journal of Science and Technology , 8 , 24, 2015. [7] Changhe, L. and Y. Shengxiang. A clustering particle swarm optimizer for dynamic optimization. in Evolutionary Computation, 2009. CEC '09. IEEE Congress on .2009, 439-446. [8] Chen, C.-Y. and Y. Fun. Particle swarm optimization algorithm and its application to clustering analysis. in Networking, Sensing and Control, 2004 IEEE International Conference on .2004, 789-794 Vol.2. [9] Chuang, L.-Y., C.-J. Hsiao, and C

Open access
Toward an Efficient Resolution for a Single-machine Bi-objective Scheduling Problem with Rejection

References [1] Y. Bartal, S. Leonardi, A. Marchetti-Spaccamela, J. Sgall, L. Stougie, “Multiprocessor scheduling with rejection”, Journal of Discrete Mathematics 13(1), 64–78, (2000). [2] J. Blazewicz, K.H. Ecker, E. Pesch, G. Schmidt, J. Weglarz, “Handbook on scheduling : From theory to applications”, Springer Berlin Heidelberg New York (2007). [3] K. Deb, S. S. Agrawal, A. Pratap, T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, Lecture Notes in Computer Sciences , 1917, 849

Open access