Abstract
Learning and evolution are two fundamental processes of adaptation. Various models have been proposed to explain their behaviour. Rather than discussing these models in detail, this paper concentrates on the interaction between learning and evolution as well as the interaction between different levels of evolution. We will argue that the evolution of learning rules and its interaction with other evolutionary developments (in either artificial or biological systems) plays a key role in accounting for the creativity of those systems. We will concentrate on two models of learning and evolution: connectionistlearning (artificial neural networks, or ANNs) and genetic algorithms (GAs).
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References
Bartlett, P., and Downs, T.: 1990, Training a neural network with a genetic algorithm, Technical Report, Department of Electrical Engineering, University of Queensland, Australia.
Belew, R. K.: 1989, Evolution, learning and culture: computational metaphors for adaptive algorithms, Technical Report #CS89–156, Computer Science and Engineering Department, University of California, San Diego.
Belew, R. K., McInerney, J. and Schraudolph, N. N.: 1991, Evolving networks: using genetic algorithm with connectionist learning, Technical Report #CS90–174, Cognitive Computer Science Research Group, Computer Science and Engineering Department, University of California, San Diego.
Bengio, Y. and Bengio, S.: 1990, Learning a synaptic learning rule, TechnicalReport 751, Départment d’Informatique et de Recherche Opérationelle, Université de Montréal.
Caudell, T. P. and Dolan, C. P.: 1989, Parametric connectivity: training of constrained networks using genetic algorithms, in Schaffer, J. D. (ed.), Proceedings 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, Washington, DC, pp. 370–374.
Chalmers, D. J.: 1990, The evolution of learning: an experiment in genetic connectionism, in Touretzky, D. S., Elman, J. L. and Hinton, G. E. (eds) Proceedings of the 1990 Connectionist Models Summer School,Morgan Kaufmann, San Mateo, pp. 81–90.
Dodd, N., Macfarlane, D., and Marland, C.: 1991, Optimisation of artificial neural network structure using genetic techniques implemented on multiple transputers, Proceedings of Transputing’91, 105, Amsterdam, pp. 687–700.
Dolan, C. P. and Dyer, M. G.: 1987, Towards the evolution of symbols, Proceedings2nd International Conference on Genetic Algorithms, Cambridge, MA, pp. 123–131.
Fogel, D. B., Fogel, L. J. and Porto, V. W.: 1990, Evolving neural networks, Biological Cybernetics, 63: 487–493.
Hancock, P. J. B.: 1990, GANNET: design of a neural net for face recognition by genetic algorithm, Technical Report R CCCN-6, Center for Cognition and Computational Neuroscience, Department of Computing Science and Psychology, Stirling University, UK.
Harp, S. A., Samad, T. and Guha, A.: 1989, Towards the genetic synthesis of neural networks, in Schaffer, J. D. (ed.), Proceedings 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, Washington, DC, pp. 360–369.
Hinton, G. E. and Nolan, S. J.: 1987, How learning can guide evolution, Complex Systems, 1: 495–502.
Kandel, E. R. and Schwartz, J. H. (eds): 1991, Principles Neural Science, 2nd edn, Elsevier, New York.
Kitano, H.: 1990, Empirical studies on the speed of convergence of neural network training using genetic algorithms, Proceedings AAAI, Boston, USA.
Kitano, H.: 1990, Designing neural networks using genetic algorithms with graph generation system, Complex Systems, 4: 461–476.
Lewin. B.: 1975, Units of transcription and translation: sequence components of heterogeneous nuclear RNA and messager RNA, Cell, 4.
Merrill, J. W. L. and Port, R. F.: 1991, Fractally configured neural networks, Neural Networks, 4: 53–60.
Miller, G. F., Todd, P. M., and Hegde, S. U.: 1989, Designing neural networks using genetic algorithms, in Schaffer, J. D. (ed.), Proceedings 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, Washington, DC, pp. 379–384.
Mjolsness, E., Sharp, D. H. and Alpert, B. K.: 1989, Scaling, machine learning, and genetic neural nets, Advances in Applied Mathematics, 10: 137–163.
Montana, D., and Davis, L.: 1989, Training feedforward neural networks using genetic algorithms
Proceedings International Joint Conference on Artificial Intelligence,pp. 762–767.
Mt hlenbein, H., and Kindermann, J.: 1989, The dynamics of evolution and learning towards genetic neural networks, in Pfeifer, R. et al. (eds), Connectionism in Perspective, North-Holland, pp. 173–198.
Nolfi, S., Elman, J. L., and Parisi, D.: 1990, Learning and evolution in neural networks, Technical Report CRT-9019, Center for Research in Language, University of California, San Diego. Rudnick, M.: 1990, A bibliography of the intersection of genetic search and artificial neural networks
Technical Report CS/E 90–001,Department of Computer Science and Engineering, Oregon Graduate Institute.
Schaffer, J. D. (ed.): 1989, Proceedings 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, Washington, DC.
Schaffer, J. D., Caruana, R. A. and Eshelman, L. J.: 1990, Using genetic search to exploit the emergent behavior of neural networks, Physica D, 42: 244–248.
Smith, J. M.: 1987, When learning guides evolution, Nature, 329: 761–762.
Weiss, G.: 1990, Combining neural and evolutionary learning: aspects and approaches, Report FM-132–90, Technische Universität Munchen, Institut fur Informatik, Germany.
Whitley, D. and Hanson, T.: 1989, Optimizing neural networks using faster, more accurate genetic search, in Schaffer, J. D., (ed.), Proceedings 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, Washington, DC, pp. 391–396.
Whitley, D., Starkweather, T. and Bogart, C.: 1990, Genetic algorithms and neural networks: optimizing connections and connectivity, Parallel Computing, 14: 347–361.
Wilson, S. W.: 1990, Perceptron redux: emergence of structure, Physica D, 42: 249–256.
Yao, X.: 1991, Optimization by genetic annealing, in Jabri, M., (ed.), Proceedings 2nd Australian Conference on Neural Networks, Sydney University, Australia, pp. 94–97.
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© 1994 Springer Science+Business Media Dordrecht
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Yao, X. (1994). The Evolution of Connectionist Networks. In: Dartnall, T. (eds) Artificial Intelligence and Creativity. Studies in Cognitive Systems, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0793-0_16
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DOI: https://doi.org/10.1007/978-94-017-0793-0_16
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