MDL-Based Fitness Functions for Learning Parsimonious Programs

method is shown in the context of evolving neural networks based on noisy training data. We also discuss the relationship of this work with other MDL based approaches to tree induction. Deriving MDL-Based Fitness Functions As outlined in the introduction, the goal of genetic programming can be formulated as finding a program or model, A, whose evaluation fA best approximates the underlying relation ], where the approximation quality is measured by N 1

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