Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks

TL;DR

This paper demonstrates a modular benchmarking environment that integrates algorithm configuration and experimental platforms, enabling systematic analysis and comparison of large classes of sampling-based optimization heuristics.

Contribution

It introduces a novel pipeline combining ParadisEO, irace, and IOHprofiler for efficient, large-scale benchmarking and analysis of algorithms on complex problems.

Findings

Fast evaluation times for algorithms

Rich data sets support detailed analysis

Standardized interface for broad algorithm benchmarking

Abstract

We present a first proof-of-concept use-case that demonstrates the efficiency of interfacing the algorithm framework ParadisEO with the automated algorithm configuration tool irace and the experimental platform IOHprofiler. By combing these three tools, we obtain a powerful benchmarking environment that allows us to systematically analyze large classes of algorithms on complex benchmark problems. Key advantages of our pipeline are fast evaluation times, the possibility to generate rich data sets to support the analysis of the algorithms, and a standardized interface that can be used to benchmark very broad classes of sampling-based optimization heuristics. In addition to enabling systematic algorithm configuration studies, our approach paves a way for assessing the contribution of new ideas in interplay with already existing operators -- a promising avenue for our research domain, which at present may have a too strong focus on comparing entire algorithm instances.