Abstract
This paper studies the characteristics of interaction among genetic evolution, individual learning and social learning using an evolutionary computation system with NK fitness landscape, both under static and dynamic environments. We show conditions for effective social learning: at least 1.5 times lighter cost of social learning than that of individual learning, beneficial teaching action, low epistasis and dynamic environment.
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References
Hinton, G.E., Nowlan, S.J.: How learning can guide evolution. Complex Systems 1, 495–502 (1987)
Mayley, G.: Landscapes, learning costs and genetic assimilation. Evolutionary Computation 4(3), 213–234 (1996)
Best, M.L.: How culture can guide evolution: an inquiry into gene/meme enhancement and opposition. Adaptive Behavior 7(3-4), 289–306 (1999)
Arita, T., Suzuki, R.: Interactions between Learning and Evolution – Outstanding Strategy generated by the Baldwin Effect. In: Proceedings of Artificial life VII, pp. 196–205 (2000)
Tomasello, M.: Cultural Origin of Human Cognition. Harvard University Press (1999)
Kauffman, S.: Adaptation on rugged fitness landscapes. In: Stein, D. (ed.) Lectures in the Sciences of Complexity, pp. 527–618. Addison-Wesley, Reading (1989)
Wright, A.H., Thompson, R.K., Zhang, J.: The computational complexity of N-K fitness functions. IEEE Transactions on Evolutionary Computation 4(4), 373–379 (2000)
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© 2008 Springer-Verlag Berlin Heidelberg
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Hashimoto, T., Warashina, K. (2008). Evolutionary Computation Using Interaction among Genetic Evolution, Individual Learning and Social Learning. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_17
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DOI: https://doi.org/10.1007/978-3-540-89197-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89196-3
Online ISBN: 978-3-540-89197-0
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