Balancing Accuracy and Parsimony in
نویسنده
چکیده
Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of xed length. The exible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary diiculty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of tness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The eeectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.
منابع مشابه
Balancing Accuracy and Parsimony in Genetic Programming
Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representa tions of variable size instead of linear strings of xed length The exible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically One primary di culty however is that the solutions may grow too big without any improvement o...
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