IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

Hao Wang (1) , Diederick Vermetten (1) , Furong Ye (1) , Carola Doerr (2) , Thomas Bäck (1)

1 LIACS - Leiden Institute of Advanced Computer Science [Leiden]
2 RO - Recherche Opérationnelle

Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, etc. This task, however, involves manual setup, execution, and analysis of the experiment on an individual basis, which is laborious and can be mitigated by a generic and well-designed platform. For this purpose, we propose IOHanalyzer, a new user-friendly tool for the analysis, comparison, and visualization of performance data of IOHs. Implemented in R and C++, IOHanalyzer is fully open source. It is available on CRAN and GitHub. IOHanalyzer provides detailed statistics about fixed-target running times and about fixed-budget performance of the benchmarked algorithms with a real-valued codomain, singleobjective optimization tasks. Performance aggregation over several benchmark problems is possible, for example in the form of empirical cumulative distribution functions. Key advantages of IOHanalyzer over other performance analysis packages are its highly interactive design, which allows users to specify the performance measures, ranges, and granularity that are most useful for their experiments, and the possibility to analyze not only performance traces, but also the evolution of dynamic state parameters. IOHanalyzer can directly process performance data from the main benchmarking platforms, including the COCO platform, Nevergrad, the SOS platform, and IOHexperimenter. An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.

Domaines: Réseau de neurones [cs.NE]

Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck. IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics. ACM Transactions on Evolutionary Learning and Optimization, 2022, 2 (1), pp.3:1--3:29. ⟨10.1145/3510426⟩. ⟨hal-03520666⟩