Abstract
Estimation of Distribution Algorithms in Genetic Programming (EDA-GP) are algorithms applying stochastic model learning to genetic programming. In spite of various potential benefits, probabilistic prototype tree (PPT) based EDA-GPs recently appeared to have a critical problem of losing diversity easily. As an alternative learning method to reduce the effect, likelihood weighting (LW) was proposed and its results were positive to improve EDA-GP performance. In this paper, we aim to provide more generalised verification results to confirm the effects of LW. We investigate performance of PPT-based EDA-GP in a large scale problem predicting arthritis using medical data.
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