Strategies for runtime prediction and mathematical solvers tuning

Barry, Michael (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Schumacher, Michael (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Mathematical solvers have evolved to become complex software and thereby have become a difficult subject for Runtime Prediction and parameter tuning. This paper studies various Machine Learning methods and data generation techniques to compare their effectiveness for both Runtime Prediction and parameter tuning. We show that machine Learning methods and Data Generation strategies that perform well for Runtime Prediction do not necessary result in better results for solver tuning. We show that Data Generation algorithms with an emphasis on exploitation combined with Random Forest is successful and random trees are effective for Runtime Prediction. We apply these methods to a hydro power model and present results from two experiments.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Prague, Czech Republic, 19-21 February 2019
Date:
2019-02
Prague, Czech Republic
19-21 February 2019
Pagination:
Pp. 669-676
Published in:
Proceedings of the 11th International Conference on Agents and Artificial Intelligence - (Volume 2)
DOI:
ISSN:
2184-433X
ISBN:
978-989-758-350-6
Appears in Collection:



 Record created 2020-05-11, last modified 2020-10-27

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