Comprehensibility and Overfitting Avoidance in Genetic Programming [1 citations — 1 self]
by
Lee A. Becker
,
Mukund Seshadri
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@MISC{Becker_comprehensibilityand,
author = {Lee A. Becker and Mukund Seshadri},
title = {Comprehensibility and Overfitting Avoidance in Genetic Programming},
year = {}
}
author = {Lee A. Becker and Mukund Seshadri},
title = {Comprehensibility and Overfitting Avoidance in Genetic Programming},
year = {}
}
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Abstract:
This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehensibility.
