Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices

TL;DR

This paper reviews recent theoretical advances in parameter control for discrete black-box optimization, demonstrating performance improvements through dynamic parameter choices in evolutionary algorithms.

Contribution

It provides a comprehensive survey and classification of recent theoretical results on parameter control mechanisms, highlighting their performance benefits.

Findings

Performance gains through dynamic parameter control

Broad range of mechanisms analyzed theoretically

Updated classification scheme for parameter control

Abstract

Parameter control aims at realizing performance gains through a dynamic choice of the parameters which determine the behavior of the underlying optimization algorithm. In the context of evolutionary algorithms this research line has for a long time been dominated by empirical approaches. With the significant advances in running time analysis achieved in the last ten years, the parameter control question has become accessible to theoretical investigations. A number of running time results for a broad range of different parameter control mechanisms have been obtained in recent years. This book chapter surveys these works, and puts them into context, by proposing an updated classification scheme for parameter control.