Automatically Knowledge Reuse for Regression Problems in Genetic Programming

Abstract

Genetic programming has been applied to various problems, and many derived methods have been proposed. Genetic Programming is one of the meta-heuristic methods that has to follow the No Free Lunch (NFL) theorem to improve accuracy. NFL theorem states the importance of using problem knowledge. GP with transfer learning has been proposed as methods of using knowledge. However, when this method solves a problem, it needs to select a source problem. Another approach is to use knowledge by multi-task learning, but it is necessary to solve multiple problems at the same time. In this paper, we propose a method of extracting knowledge from multiple source problems and selecting appropriate knowledge. This method uses an island model to extract knowledge, and a machine learning model to select knowledge. The advantage of this approach is the end-to-end method and that it does not require source problem selection: it automatically uses knowledge. The experimental results show that the proposed method higher rank of the test data than GP without transfer learning on average of 70 real-world regression problems. In addition, the proposed method performs as well as popular machine learning and has lower trial variance than the machine learning methods such as random forest, gradient boost, and XGBoost.