Fitness distance correlation of neural network error surfaces: A scalable, continuous optimization problem

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

Volumn 2167, Issue , 2001, Pages 157-166

  Gallagher, Marcus ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMBINATORIAL OPTIMIZATION; HEURISTIC ALGORITHMS; MACHINE LEARNING; NEURAL NETWORKS;

COMBINATORIAL OPTIMIZATION PROBLEMS; CONTINUOUS OPTIMIZATION PROBLEMS; FITNESS-DISTANCE CORRELATION; HEURISTIC OPTIMIZATION ALGORITHMS; HIGH DIMENSIONAL SPACES; NEURAL NETWORK TRAINING; POTENTIAL SOURCES; STUDENT TEACHERS;

OPTIMIZATION;

EID: 84948158600     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44795-4_14     Document Type: Conference Paper

References (12)

1. Altenberg, L. Fitness distance correlation analysis: An instructive counterex-ample. In Seventh International Conference on Genetic Algorithms (ICGA97) (San Francisco, CA, 1997), T. Baeck, Ed., Morgan Kauffman, pp. 57-64.
2. Boese, K. D. Models for Iterative Global Optimization. PhD thesis, University of California, Los Angeles, 1996.
3. Gallagher, M. Multi-layer perceptron error surfaces: visualization, structure and modelling. PhD thesis, Dept. Computer Science and Electrical Engineering, University of Queensland, 2000.
4. Holland, J. H. Building blocks, cohort genetic algorithms, and hyperplane-defined functions. Evolutionary Computation 8, 4 (2000), 373-391.
5. Jones, T. Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico, 1995.
6. Kauffman, S. The Origins of Order. Oxford University Press, Oxford, 1993, ch. 2, pp. 33-67.
7. Michalewicz, Z., Deb, K., Schmidt, M., and Stidsen, T. Test-case generator for nonlinear continuous parameter optimization techniques. IEEE Transactions on Evolutionary Computation 4, 3 (2000), 197-215.
8. Mühlenbein, H., Gorges-Schleuter, M., and Krämer, O. Evolution algorithms in combinatorial optimization. Parallel Computing 7 (1988), 65-85.
9. Naudts, B., and Kallel, L. A comparison of predictive measures of problem difficulty in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 4, 1 (2000), 1-15.
10. Reed, R. D., and II, R. J. M. Neural Smithing: supervised learning in feed forward artificial neural networks. The MIT Press, Cambridge, MA, 1999.
11. Whitley, D., Mathias, K., Rana, S., and Dzubera, J. Evaluating evolutionary algorithms. Artificial Intelligence 85, 1-2 (1996), 245-276.
12. Wolpert, D. H., and Macready, W. G. No free lunch theorems for search. Tech. Rep. SFI-TR-95-02-010, Santa Fe Institute, February 1995.