Abstract
The control problem of soft-landing a toy lunar module simulation is investigated in the context of neural nets. While traditional supervised back-propagation training is inappropriate for lack of training exemplars, genetic algorithms allow a controller to be evolved without difficulty: Evolution is a form of unsupervised learning. A novelty introduced in this paper is the presentation of additional renormalized inputs to the net; experiments indicate that the presence of such inputs allows precision of control to be attained faster, when learning time is measured by the number of generations for which the GA must run to attain a certain mean performance.
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References
P. J. Angeline, G. M. Saunders and J. B. Pollack, An evolutionnary algorithm that construct recurrent neural networks. To appear in IEEE Transactions on Neural Networks.
D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison Wesley, 1989.
S. A. Harp and T. Samad, Genetic synthesis of neural network architecture, in Handbook of Genetic Algorithms, L. Davis Ed., Van Nostrand Reinhold, New York, 1991.
R. Hecht-Nielsen, Neurocomputing, Addison Wesley, 1990.
J. Holland, Adaptation in natural and artificial systems, University of Michigan Press, Ann Harbor, 1975.
Empirical studies on the speed of convergence of neural network training using genetic algorithms. In Proceedings of Eight National Conference on Artificial Intelligence, AAAI-90, Vol. 2, pp 789–795, Boston, MA, 29 July–3 Aug 1990. MIT Press, Cambridge, MA.
Z. Michalewicz, Genetic Algorithms+Data Structures=Evolution Programs, Springer Verlag 1992.
D. Nguyen, B. Widrow, The truck Backer Upper: An example of self learning in neural networks, in Neural networks for Control, W. T. Miller III, R. S. Sutton, P. J. Werbos eds, The MIT Press, Cambridge MA, 1990.
N. J. Radcliffe, Equivalence Class Analysis of Genetic Algorithms, in Complex Systems 5, pp 183–205, 1991.
D. E. Rumelhart, J. L. McClelland, Parallel Distributed Processing — Exploration in the micro structure of cognition, MIT Press, Cambridge MA, 1986.
J. D. Schaffer, R. A. Caruana and L. J. Eshelman, Using genetic search to exploit the emergent behaviour of neural networks, Physica D 42 (1990), pp244–248.
M. Schoenauer, E.Ronald Neuro Genetic Truck Backer-Upper Controller, in IEEE World Conference on Computational Intelligence, Orlando 1994.
M. Schoenauer, E.Ronald Genetic Extensions of Neural Net Learning: Transfer Functions and Renormalisation Coefficients, in submitted to Artificial Evolution 94, Toulouse 1994.
Steve C. Suddarth, The symbolic-neural method for creating models and control behaviours from examples. Ph.D. dissertation. University of Washington. 1988.
Xin Yao, A Review of Evolutionary Artificial Neural Networks, in International Journal of Intelligent Systems 8, pp 539–567, 1993.
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© 1994 Springer-Verlag Berlin Heidelberg
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Ronald, E., Schoenauer, M. (1994). Genetic lander: An experiment in accurate neuro-genetic control. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_288
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DOI: https://doi.org/10.1007/3-540-58484-6_288
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