The rise and fall of learning: A neural network model of the genetic assimilation of acquired traits

Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002

Volumn 1, Issue , 2002, Pages 600-605

  Watson, James R ^a   Wiles, Janet ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS;

BALDWIN EFFECTS; GENETIC ASSIMILATION; LEARNING RATES; NETWORK CONNECTION; NEURAL NETWORK MODEL; PHENOTYPIC PLASTICITY;

CALCULATIONS;

EID: 84901411168     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2002.1006994     Document Type: Conference Paper

References (24)

1. Anderson, R. W. (1995). Learning and evolution: A quantitative genetics approach. Journal of Theoretical Biology, 175:89101.
2. Baldwin, J. M. (1896). A new factor in evolution. American Naturalist, 30:441-451 536-553.
3. Reproduced in Belew, R. K. & Mitchell, M. (Eds.), Adaptive Individuals in Evolving Populations. Addison-Wesley, Reading, MA.
4. Belew, R. K. (1990). Evolution, learning and culture: computational metaphors for adaptive search. Complex Systems, 4(l):ll-49.
5. Chalmers, D. (1990). The evolution of learning: An experiment in genetic connectionism. Proceedings of the 1990 Connectionist Summer School, pages 81-90.
6. Darwin, C. (1859). The Origin of Species by Means of Natural Selection. The Modern Library.
7. Fontanari, J. F. and Meir, R. (1990). The effect of learning on the evolution of asexual populations. Complex Systems, 4:401414.
8. French, R. and Messinger, A. (1994). Genes, phenes and the Baldwin effect: Learning and evolution in a simulated population. In Brooks, R. A. and Maes, P., editors, Artificial Life IV, pages 277-282.
9. Gruau, F. and Whitley, L. D. (1993). Adding learning to the cellular development of neural networks: Evolution and the Baldwin effect. Evolutionary Computation, l(3):213-233.
10. Harvey, I. (1993). The puzzle of the persistent question marks: A case study of genetic drift. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 15-22, San Mateo, CA. Morgan Kaufmann.
11. Hinton, G. and Nowlan, S. (1987). How learning can guide evolution. Complex Systems, 1:495-502.
12. Jones, M. and Konstam, A. (1999). The use of genetic algorithms and neural networks to investigate the Baldwin effect. In Carroll, J., Hiddad, H., Oppenheim, D., Bryant, B., and Lam-ont, G. B., editors, Proceedings of the 1999 ACM Symposium on Applied Computing, pages 275-279.
13. Ku, K. W. C. and Mak, M. W. (1998). Empirical analysis of the factors that affect the Baldwin effect. In Eiben, A. E., Back, T., Schoenauer, M., and Schwefel, H.-P., editors, Parallel Problem Solving from Nature-PPSN V, pages 481-490, Berlin. Springer.
14. Mayley, G. (1996a). The evolutionary cost of learning. In Maes, P., Mataric, M. J., Meyer, J.-A., Pollack, J., and Wilson, S. W., editors, From Animals to Animats 4-' Proceedings of the Fourth International Conference on Simulation of Adaptive behavior, pages 458-467. MIT Press.
15. Mayley, G. (1996b). Landscapes, learning costs and genetic assimilation. In Turney, P., Whitley, D., and Anderson, R., editors, Evolution, Learning and Instinct: 100 Years of the Baldwin Effect, Cambridge, Ma. MIT Press.
16. Mayley, G. (1997). Guiding or hiding: Explorations into the effects of learning on the rate of evolution. In Husbands, P. and Harvey, I., editors, The Proceedings of the Fourth European Conference on Artificial Life.
17. Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA.
18. Morgan, L. C. (1896). On modification and variation. Science, 4:733-740.
19. Osborn, H. F. (1896). Ontogenic and phylogenic variation. Science, 4:786-789.
20. Smith, J. M. (1987). When learning guides evolution. Nature, 329:761-762.
21. Todd, P. M. and Miller, G. F. (1991). Exploring adaptive agency II: Simulating the evolution of associative learning. In Meyer, J. and Wilson, S., editors, From animals to animats: Proceedings of the First International Conference on Simulation of Adaptive Behaviour, pages 306-315. MIT Press, Cambridge.
22. Waddington, C. H. (1942). Canalization of development and the inheritance of acquired characters. Nature, 150:563-565.
23. Whitley, D., Gordon, V. S., and Mathias, K. (1994). Lamar-ckian evolution, the Baldwin effect and function optimization. Lecture Notes in Computer Science, 866:6-15.
24. Wiles, J., Schulz, R., Bolland, S., Tonkes, B., and Hallinan, J. (2001). Selection procedures for module discovery: Exploring evolutionary algorithms for cognitive science. In Moore, J. D. and Stenning, K., editors, Proceedings of the 23rd Annual Conference of the Cognitive Science Society (CogSci 2001), pages 1124-1129. Lawrence Erlbaum Associates.