Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
This work addresses the problem of inefficient algorithm comparison for researchers in optimization, but it is incremental as it combines existing tools without introducing new methods.
The authors tackled the challenge of automating algorithm design by integrating three existing tools (ParadisEO, irace, and IOHprofiler) to create a benchmarking environment, resulting in a proof-of-concept that enables fast evaluation and systematic analysis of optimization heuristics.
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.