Comprehensibility & Overfitting Avoidance in Genetic Programming for Technical Trading Rules
作者： Mukund Seshadri , Lee A Becker
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摘要: 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 training data. Using pre-computed derived indicators, although it biases search, can express while retaining comprehensibility. Several of learned outperform buy and hold strategy S&P500 on testing period from 1990-2002, even taking into account transaction costs.
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