Abstract
This paper examines the effects of lifetime learning on the diversity and fitness of a population. Our experiments measure the phenotypic diversity of populations evolving by purely genetic means (population learning) and of others employing both population learning and lifetime learning. The results obtained show, as in previous work, that the addition of lifetime learning results in higher levels of fitness than population learning alone. More significantly, results from the diversity measure show that lifetime learning is capable of sustaining higher levels of diversity than population learning alone.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hinton, G.E., Nowlan, S.J.: How learning guides evolution. Complex Systems 1, 495–502 (1987)
Nolfi, S., Parisi, D.: Learning to adapt to changing environments in evolving neural networks. Adaptive Behavior 5(1), 75–97 (1996)
Floreano, D., Mondada, F.: Evolution of plastic neurocontrollers for situated agents. In: Animals to Animats, vol. 4 (1996)
Sasaki, T., Tokoro, M.: Adaptation toward changing environments: Why darwinian in nature? In: Husbands, P., Harvey, I. (eds.) Fourth European Conference on Artificial Life, pp. 145–153. MIT Press, Cambridge (1997)
Pereira, F.B., Costa, E.: How learning improves the performance of evolutionary agents: A case study with an information retrieval system for a distributed environment. In: Proceedings of the International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Models (ISAS 2001), pp. 19–23 (2001)
Watson, J., Wiles, J.: The rise and fall of learning: A neural network model of the genetic assimilation of acquired traits. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 600–605 (2002)
Curran, D., O’Riordan, C.: On the design of an artificial life simulator. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 549–555. Springer, Heidelberg (2003)
Curran, D., O’Riordan, C.: Artificial life simulation using marker based encoding. In: Proceedings of the 2003 International Conference on Artificial Intelligence (IC-AI 2003), vol. II., Las Vegas, Nevada, USA, pp. 665–668 (2003)
Mayley, G.: Guiding or hiding: Explorations into the effects of learning on the rate of evolution. In: Proceedings of the Fourth European Conference on Artificial Life, MIT Press, Cambridge (1997)
O’Reilly, U.M.: Using a distance metric on genetic programs to understand genetic operators. In: IEEEInternational Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4092–4097. IEEE Computer Society Press, Los Alamitos (1997)
De Jong, K.A.: Analysis of Behavior of a Class of Genetic Adaptive Systems. PhD thesis, The University of Michigan (1975)
Booker, L.B.: Improving the performance of genetic algorithms in classifier systems. In: Proc. of the International Conference on Genetic Algorithms and Their Applications, Pittsburgh, PA, pp. 80–92 (1985)
Collins, R.J., Jefferson, D.R.: Selection in massively parallel genetic algorithms. In: ICGA, pp. 249–256 (1991)
Darwin, C.: The Origin of Species: By Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. Bantam Press, London (1859)
Baldwin, J.M.: A new factor in evolution. American Naturalist 30, 441–451 (1896)
Lamarck, J.B.: Philosophie Zoologique. Chez Dentu, Paris (1809)
Boers, E., Borst, M., Sprinkhuizen-Kuyper, I.: Evolving Artificial Neural Networks using the “Baldwin Effect”. Technical Report TR 95-14 (1995)
Turney, P.D.: How to shift bias: Lessons from the baldwin effect. Evolutionary Computation 4(3), 271–295 (1996)
Suzuki, R., Arita, T.: The baldwin effect revisited: Three steps characterized by the quantitative evolution of phenotypic plasticity. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 395–404. Springer, Heidelberg (2003)
Arita, T., Suzuki, R.: Interactions between learning and evolution: The outstanding strategy generated by the baldwin effect. In: Proceedings of Artificial Life VII, pp. 196–205. MIT Press, Cambridge (2000)
Liu, Y., Yao, X., Higuchi, T.: Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation 4(4), 380–387 (2000)
Opitz, D.W., Shavlik, J.W.: Generating accurate and diverse members of a neural-network ensemble. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 535–541. MIT Press, Cambridge (1996)
Brown, G.: Diversity in Neural Network Ensembles. PhD thesis, University of Birmingham (2003)
Burke, E.K., Gustafson, S., Kendall, G.: Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evolutionary Computation 8(1), 47–62 (2004)
Eriksson, R.I.: An initial analysis of the ability of learning to maintain diversity during incremental evolution. In: Freitas, A.A., Hart, W., Krasnogor, N., Smith, J. (eds.) Data Mining with Evolutionary Algorithms, Las Vegas, Nevada, USA, pp. 120–124 (2000), citeseer.ist.psu.edu/eriksson00initial.html
Curran, D., O’Riordan, C.: Increasing population diversity through cultural learning. Adaptive Behavior 14(4) (2006)
Kaufmann, S.A.: Adaptation on rugged fitness landscapes. Lectures in the Sciences of Complexity 1, 527–618 (1989)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Curran, D., O’Riordan, C., Sorensen, H. (2007). An Analysis of the Effects of Lifetime Learning on Population Fitness and Diversity in an NK Fitness Landscape. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds) Advances in Artificial Life. ECAL 2007. Lecture Notes in Computer Science(), vol 4648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74913-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-74913-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74912-7
Online ISBN: 978-3-540-74913-4
eBook Packages: Computer ScienceComputer Science (R0)