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Iohexperimenter: Benchmarking Platform For Iterative Optimization Heuristics.

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Iohexperimenter: Benchmarking Platform For Iterative Optimization Heuristics.

Related Experiment Video

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

Jacob de Nobel¹, Furong Ye², Diederick Vermetten³

¹LIACS, Leiden University, the Netherlands j.p.de.nobel@liacs.leidenuniv.nl.

Evolutionary Computation

|July 24, 2023

View abstract on PubMed

Summary

This summary is machine-generated.

IOHexperimenter is a new toolbox for benchmarking optimization algorithms like local search and genetic algorithms. It offers customizable problem suites and detailed logging for efficient performance analysis.

Keywords:

Iterative optimization heuristics algorithm comparison benchmarking

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Area of Science:

Computational intelligence
Algorithm analysis
Optimization techniques

Background:

Benchmarking iterative optimization heuristics is crucial for performance evaluation.
Existing tools may lack flexibility or detailed logging capabilities.
The IOHprofiler project aims to provide a comprehensive environment for empirical algorithm analysis.

Purpose of the Study:

Introduce IOHexperimenter, the experimentation module of the IOHprofiler project.
Provide a user-friendly and customizable toolbox for benchmarking various iterative optimization heuristics.
Facilitate efficient performance analysis and comparison of optimization algorithms.

Main Methods:

IOHexperimenter offers an environment for creating customized problem suites.

It provides granular logging options for the optimization process.

The module ensures compatibility with existing data analysis tools for streamlined benchmarking pipelines.

Main Results:

IOHexperimenter enables easy benchmarking of algorithms like local search, evolutionary algorithms, and Bayesian optimization.
It provides an efficient interface between optimization problems and their solvers.
Granular logging significantly speeds up the deployment of benchmarking pipelines.

Conclusions:

IOHexperimenter is a valuable and flexible tool for researchers and practitioners in iterative optimization.
Its customizable nature and detailed logging enhance the efficiency and depth of algorithm benchmarking.
The module integrates seamlessly with the IOHprofiler environment, promoting reproducible research.