集成模块化基准框架的大规模自动化算法设计
Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
摘要：
我们提出了第一个概念验证用例，演示了算法框架ParadisEO与自动算法配置工具Irace和实验平台IOHprofiler的接口效率。通过结合这三个工具，我们获得了一个强大的基准测试环境，使我们能够系统地分析大型类的算法在复杂的基准测试问题。我们的管道的主要优势是快速评估时间，生成丰富数据集以支持算法分析的可能性，以及可用于对非常广泛的基于采样的优化算法进行基准测试的标准化接口。除了使系统的算法配置研究，我们的方法铺平了道路，以评估新的想法与现有的运营商的相互作用的贡献-一个有前途的途径，我们的研究领域，目前可能有一个太强的重点比较整个算法实例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
