Dynamic Algorithm Portfolios

Matteo Gagliolo, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


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.
Original languageEnglish (US)
Title of host publication9th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2006
StatePublished - Dec 1 2006
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


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