TY  - CONF
T1  - Strategies for Runtime Prediction and Mathematical Solvers Tuning
A1  - Barry, Michael
A1  - Schumann, René
TI  - Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019)
Y1  - 2019
UR  - http://insticc.org/node/TechnicalProgram/icaart/presentationDetails/73876
M2  - doi: DOI: 10.5220/0007387606690676
KW  - Evolutionary algorithm
KW  - Genetic Algorithm
KW  - machine learning
KW  - Mathematical solvers
KW  - Mixed Integer Problems
KW  - Novelty search
KW  - optimization
KW  - Runtime Prediction
KW  - Tuning mathematical solvers
N2  - 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.
ER  -