Authors
Byoung-Tak Zhang, Heinz Mühlenbein
Publication date
1995/3/1
Journal
Evolutionary Computation
Volume
3
Issue
1
Pages
17-38
Publisher
MIT Press
Description
Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this article 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 fitness functions with error and complexity terms. An adaptive learning …
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BT Zhang, H Mühlenbein - Evolutionary Computation, 1995