Explainable Benchmarking for Iterative Optimization Heuristics
This addresses the need for more comprehensive and unbiased benchmarking in iterative optimization heuristics, which is incremental as it builds on existing modular frameworks.
The paper tackles the problem of incomplete and biased benchmarking in heuristic optimization by introducing the IOH-Xplainer software framework, which systematically analyzes algorithm performance and hyper-parameter impacts across diverse scenarios, offering transparent insights for better algorithm design.
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristic optimization algorithms, only a very limited number of scenarios, algorithm configurations and hyper-parameter settings are explored, leading to incomplete and often biased insights and results. This paper presents a novel approach we call explainable benchmarking. Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters. We showcase the framework in the context of two modular optimization frameworks. Through this framework, we examine the impact of different algorithmic components and configurations, offering insights into their performance across diverse scenarios. We provide a systematic method for evaluating and interpreting the behaviour and efficiency of iterative optimization heuristics in a more transparent and comprehensible manner, allowing for better benchmarking and algorithm design.