IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics

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

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

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler environment. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. Its logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.

Mots clés: Iterative Optimization Heuristics; Benchmarking; Algorithm Comparison

Domaines: Informatique [cs]

Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr, et al.. IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. Evolutionary Computation, In press, pp.1-6. ⟨10.1162/evco_a_00342⟩. ⟨hal-04180576⟩