Using learning to facilitate the evolution of features for recognizing visual concepts

Evolutionary Computation

Volumn 4, Issue 3, 1996, Pages 297-311

  Bala, J ^a  

^a Datamat Systems Research Inc   (United States)

Author keywords

Baldwin effect; Decision trees; Evolutionary computation; Feature selection; Genetic algorithms; Hybrid systems; Induction; Pattern recognition

Indexed keywords

EID: 0000751098     PISSN: 10636560     EISSN: None     Source Type: Journal    
DOI: 10.1162/evco.1996.4.3.297     Document Type: Article

References (16)

1. Bala, J., De Jong, K., Huang, J., Vafaie, H., & Wechsler, H. (1995). Hybrid learning using genetic algorithms and decision trees for pattern classification. In C. S. Mellish (Ed.), I4th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada (pp. 719-724). San Mateo, CA: Morgan Kaufmann.
2. Bala, J., De Jong, K., Huang, J., Vafaie, H., & Wechsler, H. (1996). Visual routine for eye detection using a hybrid genetic architecture. In R. S. Ledly (Ed.), 13th International Conference on Pattern Recognition (ICPR), Vienna, Austria (pp. 1323-1334). Oxford, UK: Pergamon Press.
3. Bala, J., Pachowicz, P., & De Jong, K. (1994). Multistrategy learning from engineering data by integrating inductive generalization and genetic algorithms. In R. S. Michalski & G. Tecuci (Eds.), Machine Learning: A multistrategy approach, Volume IV (pp. 121-138). San Mateo, CA: Morgan Kaufmann.
4. Bala, J., & Wechsler, H. (1996). Shape analysis using multistrategy learning. Pattern Recognition, 29, 1323-1334.
5. Balakrishnan, K., & Honavar, V. (1995). Evolutionary design of neural architectures: A preliminary taxonomy and guide to the literature (Tech. Rep. No. CS TR 95-01). Ames, LA: Iowa State University, Artificial Intelligence Research Group, Department of Computer Science, Iowa State University.
6. Belew, R. K., & Mitchell, M. (Eds.) (1996). Adaptive individuals in evolving populations: Models and algorithms. Reading, MA: Addison-Wesley.
7. Grefenstette, J., David, L., & Cerys, D. (1991). Genesis and OOGA: Two Genetic Algorithms systems. Melrose, MA: TSP.
8. Gruau, F., & Whitley, D. (1993). Adding learning to the cellular development of neural networks: Evolution and the Baldwin Effect. Evolutionary Computation. 1, 213-234.
9. Gutta, S., Huang, J., Shah, I., Singh, D., Takacs, B., & Wechsler, H. (1995). Benchmark studies on face recognition. In M. Bichel (Ed.), International Workshop on Automatic Face and Gesture Recognition (pp. 198-209). Zurich: Computer Science Department, Multi Media Laboratory.
10. Gutta, S., & Wechsler, H. (1997). Face recognition using hybrid classifiers. Pattern Recognition (in press).
11. Hinton, G. E., & Nolan, S. J. (1987) How learning can guide evolution. Complex Systems, 1, 495-502.
12. Johnson, M. P., Maes, P., & Darell, T. (1994). Evolving visual routines. In R. A. Brooks & P. Maes (Eds.), Artificial Life IV (pp. 198-209). Cambridge, MA: MIT Press.
13. Kohavi, R., & John, G. (1995). Wrappers for feature subset selection (Tech. Rep.). Stanford, CA: Stanford University, Computer Science Department.
14. Linsker, R. (1988). Self-organization in a perceptual network. Computer, 21, 105-117.
15. Mühlenbein, H., & Kinderman, J. (1989). The dynamics of evolution and learning: Toward genetic neural networks. In R. Pfeifer, Z. Schreter, F. Fogelman-Soulie, & L. Steels (Eds.), Connectionisjn in perspective (pp. 173-197), New York: Elsevier.
16. Quinlan, J. R. (1986). The effect of noise on concept learning. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence approach (pp. 149-166). San Mateo, CA: Morgan Kaufmann.