Genetic learning from experience

2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

Volumn 3, Issue , 2003, Pages 2118-2125

  Louis, Sushil J ^a  

^a University of Nevada   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADDERS; ALGORITHMS; DESIGN; GENETIC ALGORITHMS;

BETTER PERFORMANCE; CONFIGURATION DESIGNS; DESIGN PROBLEMS; DESIGN SOLUTIONS; EVOLUTIONARY COMPUTING; GENETIC LEARNING; LONG TERM MEMORY; PERFORMANCE GAIN;

PROBLEM SOLVING;

EID: 84901457113     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2003.1299934     Document Type: Conference Paper

References (32)

1. Ackley, D. A. (1987)., A Connectionist Machine for Genetic Hillclimbing. Kluwer Academic Publishers.
2. Bearpark, K. and Keane, A. J. (2000). Short term memory in gp. In Proceedings of fourth Adaptive Computing in Design and Manufacturing, pages 309 - 320.
3. Caruana, R. (1997a). Multitask Learning. PhD thesis, School of Computer Science, Carnegie Mellon University. Pittsburg, PA.
4. Caruana, R. ( 1997b). Multitask learning: A knowledge-based source of inductive bias. Machine Learning, 28:41 -75.
5. Caruana, R., Baluja, S., and Mitchell, T. (1996). Using the future to "Sort Out" the present, rankprop and multitask learning for medical risk prediction. In Advances in Neural Information Processing Systems 8, (Proceedings of NIPS-95), pages 959-965.
6. Eshelman, L. J. (1991). The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In Rawlins, G. J. E, editor, Foundations of Genetic Algorithms, 1. pages 265-283. Morgan Kauffman.
7. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
8. Grefensttete, J. and Ramsey, C, (1992). An approach to anytime learning. In Pro-ceedings of the Ninth International Conference on Machine Learning, pages 189-195, San Mateo, California. Morgan Kauffman.
9. Holland, J. (1975). Adaptation In Natural and Anificial Systems. The University of Michigan Press, Ann Arbour.
10. Huhns, M. and Acosta, R. (1992). Argo: An analogical reasoning system for solving design problems. In Tong, C. and Sriram, D., editors, Artificial Intelligence in Engineering Design, Vol II, pages 105-144. Academic Press, Inc.
11. Jin, X. and Reynolds, R. (1999). Using knowledge based evolutionary computation to solve machine constraint optimization problems: A cultural algorithm approach. In Proceedings of the Congress on Evolutionary Computation, pages 672 -678.
12. Koza, J. R. (1993). Genetic Programming. MIT Press.
13. Leake, D. B. (1996). Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI/MIT Press, Menlo Park, CA.
14. Louis, S. J. (2002). Learning from experience: Case injected genetic algorithm design of combinational logic circuits. In Parmee. I. C., editor, Proceedings of the Fifth International Conference on Adaptive Computing in Design and Manufacturing, pages 295-306, Springer-Verlag.
15. Louis, S. J. and Johnson, J. (1997a). Solving similar problems using genetic al-gorithms and case-based memory. In Proceedings of the Seventh International Conference on Genetic Algorithms, pages 283-290. Morgan Kauffman, San Mateo, CA, .
16. Louis, S. J. and Johnson, J. (1997b). Solving similar problems using genetic algorithms and case-based memoryun, pages 101-127.
17. Louis, S. J. and Li, G. (1997). Combining robot control strategies using genetic algorithms with memory. Lecture Notes in Computer Science, Evolutionary Programming, VI, 1213:431-442.
18. Louis, S. J., McDonnell, J., and Gizzi, N. (July 2002). Dynamic strike force asset allocation using genetic algorithms and case-based reasoning. In Proceedings of the Sixth Conference on Systemics, Cybernetics, and Informatics, page 5 pages.
19. Louis, S. J., McGraw, G and Wyckoff, R. (1993). Case-based reasoning assisted explanation of genetic algorithm results. Journal of Experimental and Theoretical Artificial Intelligence, 5:21-37.
20. Louis, S. J. and Rawlins, G. J. E. (1991). Designer genetic algorithms: Genetic algo-rithms in structure design. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 53-60. Morgan Kauffman, San Mateo, CA.
21. Louis, S. J. and Zhang, Y. (1999). A sequential similarity metric for case injected genetic algorithms. In Proceedings of the 1999 Genetic and Evolutionary Computations Conference. Morgan Kauffmann.
22. McDonnell, J., Gizzi, N., and Louis, S. J. (June 2002). Strike force asset allocation using genetic search. In Proceedings of the International Conference on AI-02, page 5 pages.
23. Mitchell, T. M. (1997). Machine Learning. WCB McGraw-Hill, Boston, MA.
24. Mostow, J., Barley, M., and Weinrich, T. (1992). Automated reuse of design plans in bogart. In Tong, C. and Sriram, D., editors, Artificial Intelligence in Engineering Design, Vol II, pages 57-104. Academic Press, Inc.
25. Perez, E. I., Coello, C. C., and Aguirre, A. H. (2001). Extraction of design patterns patterns from evolutionary algorithms using case-based reasoning. In et al, Y. L., editor, Evolvable Systems: From Biology to Hardware (ICES 2001), Lecture Notes in Computer Science No. 2210, pages 244-255. Springer.
26. Ramsey, C. and Grefensttete, J, (1993). Case-based initialization of genetic al-gorithms. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 84-91, San Mateo, California. Morgan Kauffman.
27. Riesbeck, C. K. and Schank, R. C. (1989). Inside Case-Based Reasoning. Lawrence Erlbaum Associates, Cambridge, MA.
28. Schoenauer, M. and Xanthakis, S. (1993). Constrained ga optimization. In Proceedings of the Fifth InternationalConference on Genetic Algorithms, pages 573-580. Morgan Kauffman, San Mateo, CA.
29. Sheppard, J. W. and Salzburg, S. L. (1995). Combining genetic algorithms with memory based reasoning. In Forrest, S., editor, Proceedings of the Sixth International Conference on Genetic Algorithms, pages 452-459, San Mateo, California. Morgan Kauffman.
30. Thomson, P. (2000). Circuit evolution and visualization. In et al, J. M., editor, Evolvable Systems: From Biology to Hardware (ICES 2000), Lecture Notes in Computer Science, pages 229 -240. Springer.
31. Thrun, S. (1996a). Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Kluwer Academic Publisher, Amsterdam.
32. Thrun, S. (1996b). Is learning the n-th thing any easier than learning the first. In Advances in Neural Information Processing Systems 8, (Proceedings of the NIPS-95), pages 640-646.