Abstract
Many learning paradigms for neural networks are based on the optimization of some measure of performance. The learning behavior of these networks then depends on the topography of the corresponding “fitness landscape” and on the chosen optimization method. In this paper, we apply different learning strategies to a number of Boolean problems and analyze how the topography of the fitness landscape is affected by the complexity of the problem and by the network architecture. We further discuss the application of stochastic optimization strategies to problems with delayed performance estimation and present results for a neurocontrol solution to the “lunar lander” problem.
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Bernasconi, J. (1990). Learning and optimization. In: Garrido, L. (eds) Statistical Mechanics of Neural Networks. Lecture Notes in Physics, vol 368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540532676_45
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DOI: https://doi.org/10.1007/3540532676_45
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