Pith. sign in

REVIEW 3 cited by

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2111.04077 v2 pith:EZ2SX6DY submitted 2021-11-07 cs.NE

Jacob de Nobel , Furong Ye , Diederick Vermetten , Hao Wang , Carola Doerr , Thomas Bäck This is my paper · ORCID
classification cs.NE
keywords iohexperimenteroptimizationbenchmarkinganalysiscomponentsdataheuristicsinteractive
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present IOHexperimenter, the experimentation module of the IOHprofiler project, which aims at providing an easy-to-use and highly customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, Bayesian optimization techniques, etc. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer, the module for interactive performance analysis and visualization. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. These 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Does Dimensionality Reduction via Random Projections Preserve Landscape Features?

    cs.LG 2026-04 unverdicted novelty 6.0 of 10

    Random projections frequently distort most ELA features, so reduced-dimensional versions no longer represent the original landscape's geometric and topological properties.

  2. NeurOptimisation: The Spiking Way to Evolve

    cs.NE 2025-07 conditional novelty 6.0 of 10

    NeurOptimiser uses populations of spiking neurons to run heuristic search, solving BBOB benchmark functions up to 40 dimensions with estimated low power consumption.

  3. instancespace: a Python Package for Insightful Algorithm Testing through Instance Space Analysis

    cs.SE 2025-01 conditional novelty 3.0 of 10

    instancespace is a modular Python implementation of the Instance Space Analysis pipeline, replicating the MATLAB ISA toolkit.

Pith tools