Benchmarking Discrete Optimization Heuristics with IOHprofiler

TL;DR

This paper evaluates the IOHprofiler benchmarking environment by comparing 12 heuristics across 23 discrete optimization problems, demonstrating its effectiveness for automated performance analysis and introducing new aggregated performance metrics.

Contribution

The work showcases IOHprofiler's capabilities for benchmarking discrete heuristics and introduces ECDF-based performance summaries for problem groups.

Findings

IOHprofiler effectively supports automated benchmarking.

Twelve heuristics were systematically compared.

New ECDF module enables aggregated performance analysis.

Abstract

Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons provide insights into the strengths and weaknesses of different approaches, which can be leveraged into designing new algorithms and into the automation of algorithm selection and configuration. With the ultimate goal to create a meaningful benchmark set for iterative optimization heuristics, we have recently released IOHprofiler, a software built to create detailed performance comparisons between iterative optimization heuristics. With this present work we demonstrate that IOHprofiler provides a suitable environment for automated benchmarking. We compile and assess a selection of 23 discrete optimization problems that subscribe to different types of fitness landscapes. For each selected problem we compare performances of twelve different heuristics, which are as of now available as baseline algorithms in IOHprofiler. We also provide a new module for IOHprofiler which extents the fixed-target and fixed-budget results for the individual problems by ECDF results, which allows one to derive aggregated performance statistics for groups of problems.