A memetic pareto evolutionary approach to artificial neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2256, Issue , 2001, Pages 1-12

  Abbass, H A ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Genetic algorithms; Neural networks

Indexed keywords

GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION;

DIFFERENTIAL EVOLUTION; EVOLUTIONARY APPROACH; EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS; FOCUS OF RESEARCHES; LOCAL SEARCH; MEMETIC;

NEURAL NETWORKS;

EID: 84944892396     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45656-2_1     Document Type: Conference Paper

References (32)

1. H.A. Abbass, R. Sarker, and C. Newton. A pareto differential evolution approach to vector optimisation problems. Congress on Evolutionary Computation, 2:971–978, 2001.
2. C.L. Blake and C.J. Merz. UCI repository of machine learning databases, http://www.ics.uci.edu/∼mlearn/mlrepository.html. University of California, Irvine, Dept. of Information and Computer Sciences, 1998.
3. C.A. Coello. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, 1(3):269–308, 1999.
4. S. Fahlman and C. Lebiere. The cascade correlation learning architecture. Technical Report CMU-CW-90-100, Canegie Mellon University, Pittsburgh, PA, 1990.
5. D.B. Fogel. Evolutionary Computation: towards a new philosophy of machine intelligence. IEEE Press, New York, NY, 1995.
6. D.B. Fogel, E.C. Wasson, and E.M. Boughton. Evolving neural networks for detecting breast cancer. Cancer letters, 96(1):49–53, 1995.
7. D.B. Fogel, E.C. Wasson, and V.W. Porto. A step toward computer-assisted mammography using evolutionary programming and neural networks. Cancer letters, 119(1):93, 1997.
8. S. Haykin. Neural networks - a comprehensive foundation. Printice Hall, USA, 2 edition, 1999.
9. J. Horn, N. Nafpliotis, and D.E. Goldberg. A niched pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation, 1:82–87, 1994.
10. D.J. Janson and J.F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Systems Engineering, 2:272–276, 1992.
11. D.J. Janson and J.F. Frenzel. Training product unit neural networks with genetic algorithms. IEEE Expert, 8(5):26–33, 1993.
12. H. Kitano. Designing neural networks using genetic algorithms with graph generation system. Complex Systems, 4(4):461–476, 1990.
13. J. Knowles and D. Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2):149–172, 2000.
14. Y. LeCun, J.J. Denker, and S.A. Solla. Optimal brain damage. In D. Touretzky, editor, Advances in Neural Information Processing Systems. Morgan Kaufmann, 1990.
15. V. Maniezzo. Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks, 5(1):39–53, 1994.
16. F. Menczer and D. Parisi. Evidence of hyperplanes in the genetic learning of neural networks. Biological Cybernetics, 66:283–289, 1992.
17. D. Michie, D.J. Spiegelhalter, and C.C. Taylor. Machine learning, neural and statistical classification. Ellis Horwood, 1994.
18. P. Moscato. Memetic algorithms: a short introduction. In D. Corne, M. Dorigo, and F. Glover, editors, New ideas in optimization, pages 219–234. McGraw-Hill, 1999.
19. V.W. Porto, D.B. Fogel, and L.J. Fogel. Alternative neural network training methods. IEEE Expert, 10(3):16–22, 1995.
20. J.C.F. Pujol and R. Poli. Evolving the topology and the weights of neural networks using a dual representation. Applied Intelligence, 8(1):73–84, 1998.
21. D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning internal representations by error propagation. In J.L. McClelland D.E. Rumelhart and the PDP Research Group Eds, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition., Foundations, 1, 318,. MIT Press Cambridge, 1986.
22. R. Sarker, H.A. Abbass, and C. Newton. Solving multiobjective optimization problems using evolutionary algorithm. The International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA’2001), Los Vegas, USA, 2001.
23. J.D. Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pages 93–100, 1985.
24. R. Storn and K. Price. Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, 1995.
25. P. Werbos. Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University, 1974.
26. W. Yan, Z. Zhu, and R. Hu. Hybrid genetic/bp algorithm and its application for radar target classification. Proceedings of the 1997 IEEE National Aerospace and Electronics Conference, NAECON, pages 981–984, 1997.
27. X. Yao. Evolutionary artificial neural networks. International Journal of Neural Systems, 4(5):203–222, 1993.
28. X. Yao. A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 8(4):529–567, 1993.
29. X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423–1447, 1999.
30. X. Yao and Y. Liu. Making use of population information in evolutionary artificial neural networks. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 28(3):417–425, 1998.
31. X. Yao and Y. Liu. Towards designing artificial neural networks by evolution. Applied Mathematics and Computation, 91(1):83–90, 1998.
32. E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999.