Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler

TL;DR

This paper integrates the dynamic binary value problem into IOHprofiler, enabling large-scale benchmarking experiments that connect theoretical insights with empirical performance of genetic algorithms in dynamic environments.

Contribution

It introduces the integration of the dynamic binary value problem into IOHprofiler, facilitating comprehensive benchmarking and bridging theory with empirical analysis.

Findings

Recreated theoretical results on moderate dimensions

Identified performance aspects of GAs in dynamic settings

Highlighted synergies between theory and benchmarking

Abstract

Optimization problems in dynamic environments have recently been the source of several theoretical studies. One of these problems is the monotonic Dynamic Binary Value problem, which theoretically has high discriminatory power between different Genetic Algorithms. Given this theoretical foundation, we integrate several versions of this problem into the IOHprofiler benchmarking framework. Using this integration, we perform several large-scale benchmarking experiments to both recreate theoretical results on moderate dimensional problems and investigate aspects of GA's performance which have not yet been studied theoretically. Our results highlight some of the many synergies between theory and benchmarking and offer a platform through which further research into dynamic optimization problems can be performed.