Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification

arXiv (Cornell University)

Abstract

In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of randomly generated functions (RGF) for the model training, which cover a much more diverse set of optimization problem classes compared to the widely-used black-box optimization benchmarking (BBOB) suite. Correspondingly, we focus on automated algorithm configuration (AAC), that is, selecting the best suited algorithm and fine-tuning its hyperparameters based on the landscape features of problem instances. Precisely, we analyze the performance of dense neural network (NN) models in handling the multi-output mixed regression and classification tasks using different training data sets, such as RGF and many-affine BBOB (MA-BBOB) functions. Based on our results on the BBOB functions in 5d and 20d, near optimal configurations can be identified using the proposed approach, which can most of the time outperform the off-the-shelf default configuration considered by practitioners with limited knowledge about AAC. Furthermore, the predicted configurations are competitive against the single best solver in many cases. Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.

Authors 5

1. Fu Xing Long Aachen

   Leiden University · BMW Group (Germany)

   Affiliation as printed

   BMW Group , Knorrstraße 147 , 80788 Munich , Germany

   LIACS , Leiden University , Niels Bohrweg 1 , 2333 Leiden , Netherlands

2. Moritz A. Frenzel
   Affiliation as printed

   Altair Engineering GmbH , Calwer Straße 7 , 71034 Böblingen , Germany

3. Markus Gitterle
   Affiliation as printed

   Munich University of Applied Sciences , Dachauer Straße 98b , 80335 Munich , Germany

4. Thomas Bäck
5. Bas van Stein Aachen

   Leiden University

   Affiliation as printed

   LIACS , Leiden University , Niels Bohrweg 1 , 2333 Leiden , Netherlands