Evolutionary algorithms for optimization: Do we get effective solutions?

Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03

Volumn 1, Issue , 2003, Pages 404-408

  Sarker, Ruhul A ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EFFECTIVE SOLUTION; EVOLUTIONARY COMPUTATIONS; FOCAL POINTS; MATHEMATICAL FOUNDATIONS; OPTIMISATIONS; OPTIMIZATION TOOLS; REAL-WORLD PROBLEM;

MATHEMATICAL PROGRAMMING; MATHEMATICAL TECHNIQUES; NEURAL NETWORKS; SIGNAL PROCESSING;

EVOLUTIONARY ALGORITHMS;

EID: 78650088606     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNNSP.2003.1279294     Document Type: Conference Paper

References (18)

1. W. Barnett, C. Chiarella, S. Keen, R. Marks, and H. Schnabl, Complexity and Evolution, Cambridge University Press, 2000.
2. K. De Jong, Evolving in a Changing World. In Z. Ras and A. Skoworn (eds.), Foundation of Intelligent Systems, Lecture Notes in Artificial Intelligence 1609, Springer, pp513-519, 1999.
3. A. Eiben, R. Hinterding and Z. Michalewicz, Parameter Control in Evolutionary Algorithms, IEEE Transactions on Evolutionary Computation, 3(2), pp124-141, 1999.
4. M. Fischer and Y. Leung, Geo Computational Modelling Techniques and Applications, Springer-Verlag, Berlin, 2001.
5. D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.
6. J. Grefenstette, Evolvability in Dynamic Fitness Landscape: A Genetic Algorithm Approach, IEEE Congress on Evolutionary Computation, pp2031-2038, 1999.
7. T. Hanne, On the convergence of multi-objective evolutionary algorithms, European Journal of Operational Research, 117(3), pp553-564, 1999.
8. J. He and L. Kang, On the Convergence Rates of Genetic Algorithms, Theoretical Computer Science, 229, pp23-39, 1999.
9. M. Koksalan and A. B. Keha, Using Genetic Algorithms for Single-Machine Bicriteria Scheduling Problems, European Journal of Operational Research, 145, pp543-556, 2003.
10. R. Morrison and K. De Jong, A Test Problem Generator for Non-Stationary Environments, IEEE Congress on Evolutionary Computation, pp2047-2053, 1999.
11. M. Patel, V. Honavar, and K. Balakrishnan, Advances in the Evolutionary Sysnthesis of Intelligent Agents, The MIT Press, Cambridge, MA, 2001.
12. M. Sakawa and K. Kato, Genetic Algorithms with Double Strings for 0-1 Programming Problems, European Journal of Operational Research, 144, pp581-597, 2003.
13. S. Thangiah and K. Nygard, MICAH: A Genetic Algorithm System for Multi-Commodity Transshipment Problems, Proc. Of the Eighth Conference on Artificial Intelligence for Applications, pp240-246, 1992.
14. Y. Takahashi, Convergence of Simple Genetic Algorithms for the Two-bit Problem, BioSystems, 46, pp235-282, 1998.
15. R, Varela, C. Vela, J, Puente and A Gomez, A Knowledge-Based Evolutionary Strategy for Scheduling Problems with Bottlenecks, European Journal of Operational Research, 145, pp57-71, 2003.
16. X. Yao (edited), Evolutionary Computation: Theory and Application, World Scientific Publ. Co., Singapore, 1999.
17. X. Yao, Evolutionary Computation: A Gentle Introduction, In Evolutionary Optimization, R. Sarker, M. Mohammadian and X. Yao (edited), Kluwer, USA, pp27-56, 2002.
18. E. Zitzler, K. Deb, and L. Thiele, Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary Computation, 8(2), pp.173-195, 2000.