6533b82efe1ef96bd1292619

RESEARCH PRODUCT

Improving estimation of distribution genetic programming with novelty initialization

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Estimation of distribution genetic programming (EDA-GP) replaces the standard variation operations of genetic programming (GP) by learning and sampling from a probabilistic model. Unfortunately, many EDA-GP approaches suffer from a rapidly decreasing population diversity which often leads to premature convergence. However, novelty search, an approach that searches for novel solutions to cover sparse areas of the search space, can be used for generating diverse initial populations. In this work, we propose novelty initialization and test this new method on a generalization of the royal tree problem and compare its performance to ramped half-and-half (RHH) using a recent EDA-GP approach. We find that novelty initialization provides a higher diversity than RHH and the EDA-GP also achieves a better average fitness using novelty initialization.