@article{Zhang-Muehlenbein-95-ECJ,
title = {Balancing Accuracy and Parsimony in Genetic
Programming},
author = {Byoung-Tak Zhang and Heinz M{\"u}hlenbein},
journal = {Evolutionary Computation},
number = {1},
pages = {17--38},
url = {http://www.ais.fraunhofer.de/~muehlen/publications/gmd_as_ga-94_09.ps},
volume = {3},
year = {1995},
abstract = {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 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 fitness 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
effectiveness 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.},
doi = {doi:10.1162/evco.1995.3.1.17},
keywords = {Bayesian Evolving Machine Minimum Tree algorithms, comparison, description genetic induction, learning, length model networks. neural principle, programming, }
}
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