Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms

TL;DR

This paper investigates how parameter tuning affects the performance of various nature-inspired algorithms across different problem landscapes, providing insights into when tuning is beneficial.

Contribution

It introduces a problem-agnostic approach to analyze the impact of parameter tuning on multiple algorithms across diverse fitness landscapes.

Findings

Certain algorithms benefit significantly from tuning in specific landscape conditions.

Tuning effects vary depending on landscape complexity and algorithm type.

Guidelines for when to tune parameters are derived from landscape characteristics.

Abstract

The problem of parameterization is often central to the effective deployment of nature-inspired algorithms. However, finding the optimal set of parameter values for a combination of problem instance and solution method is highly challenging, and few concrete guidelines exist on how and when such tuning may be performed. Previous work tends to either focus on a specific algorithm or use benchmark problems, and both of these restrictions limit the applicability of any findings. Here, we examine a number of different algorithms, and study them in a "problem agnostic" fashion (i.e., one that is not tied to specific instances) by considering their performance on fitness landscapes with varying characteristics. Using this approach, we make a number of observations on which algorithms may (or may not) benefit from tuning, and in which specific circumstances.