Traditional Meta-Learning requires long training times, and is often focused on optimizing performance quality, neglecting computational complexity. Algorithm Portfolios are more robust, but present similar limitations. We reformulate algorithm selection as a time allocation problem: all candidate algorithms are run in parallel, and their relative priorities are continually updated based on runtime information, with the aim of minimizing the time to reach a desired performance level. Each algorithm's priority is set based on its current time to solution, estimated according to a parametric model that is trained and used while solving a sequence of problems, gradually increasing its impact on the priority attribution. The use of censored sampling allows to train the model efficiently.
|Title of host publication
|9th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2006
|Published - Dec 1 2006