Sambo, Aliyu Sani; Azad, R Muhammad Atif; Kovalchuk, Yevgeniya; Padmanaabhan, Vivek; Shah, Hanifa; (2021) Improving the generalisation of genetic programming models with evaluation time and asynchronous parallel computing. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. (pp. pp. 265-266). ACM (Association for Computing Machinery)

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Abstract

In genetic programming (GP), controlling complexity often means reducing the size of evolved expressions. However, previous studies show that size reduction may not avoid model overfitting. Therefore, in this study, we use the evaluation time --- the computational time required to evaluate a GP model on data --- as the estimate of model complexity. The evaluation time depends not only on the size of evolved expressions but also their composition, thus acting as a more nuanced measure of model complexity than size alone. To constrain complexity using this measure of complexity, we employed an explicit control technique and a method that creates a race condition. We used a hybridisation of GP and multiple linear regression (MLRGP) that discovers useful features to boost training performance in our experiments. The improved training increases the chances of overfitting and facilitates a study of how managing complexity with evaluation time can address overfitting. Also, MLRGP allows us to observe the relationship between evaluation time and the number of features in a model. The results show that constraining evaluation time of MLRGP leads to better generalisation than both plain MLRGP and with an effective bloat-control.

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