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GECCO
2008
Springer

Accelerating genetic programming by frequent subtree mining

8 years 10 months ago
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
Yoshitaka Kameya, Junichi Kumagai, Yoshiaki Kurata
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Where GECCO
Authors Yoshitaka Kameya, Junichi Kumagai, Yoshiaki Kurata
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