Improving GP classification performance by injection of decision trees

This paper presents a novel hybrid method combining genetic programming and decision tree learning. The method starts by estimating a benchmark level of reasonable accuracy, based on decision tree performance on bootstrap samples of the training set. Next, a normal GP evolution is started with the aim of producing an accurate GP. At even intervals, the best GP in the population is evaluated against the accuracy benchmark. If the GP has higher accuracy than the benchmark, the evolution continues normally until the maximum number of generations is reached. If the accuracy is lower than the benchmark, two things happen. First, the fitness function is modified to allow larger GPs, able to represent more complex models. Secondly, a decision tree with increased size and trained on a bootstrap of the training data is injected into the population. The experiments show that the hybrid solution of injecting decision trees into a GP population gives synergetic effects producing results that are b...

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Rikard König, Ulf Johansson, Tuve Löfström, Lars Niklasson
• Keywords: Artificial Intelligence, CEC 2010, Decision Tree, Normal Gp, Normal Gp Evolution