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

TL;DR

This paper explores landscape-aware algorithm configuration using neural networks trained on diverse problem instances generated by RGF and BBOB functions, achieving near-optimal configurations that outperform defaults and are competitive with the best solvers.

Contribution

It introduces a novel approach combining RGF and BBOB functions for training neural networks to improve automated algorithm configuration accuracy.

Findings

Neural network models effectively predict high-performing configurations.

Training on combined RGF and BBOB data yields better configurations.

Approach outperforms default settings and competes with best solvers.

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.