Accelerating genetic programming by frequent subtree mining

One crucial issue in genetic programming (GP) is how to acquire promising building blocks efficiently. In this paper, we propose a GP method (called GPTM, GP with Tree Mining) which protects the subtrees repeatedly appearing in superior individuals. Currently GPTM utilizes a FREQT-like efficient data mining method to find such subtrees. GPTM is evaluated by three benchmark problems, and the results indicate that GPTM is comparable to or better than POLE, one of the most advanced probabilistic model building GP methods, and finds the optimal individual earlier than the standard GP and POLE. Categories and Subject Descriptors

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Yoshitaka Kameya, Junichi Kumagai, Yoshiaki Kurata
• Keywords: Building Gp Methods, GECCO 2008, Gp Methods, Optimization, Promising Building Blocks