Assessing Ranking and Effectiveness of Evolutionary Algorithm Hyperparameters Using Global Sensitivity Analysis Methodologies

TL;DR

This paper conducts a comprehensive global sensitivity analysis on various evolutionary algorithms to understand how hyperparameters influence their performance, interactions, and stability, guiding better tuning practices.

Contribution

It introduces a systematic framework applying three sensitivity analysis methods to evaluate hyperparameter influence in multiple evolutionary algorithms, revealing their behaviors and importance.

Findings

Hyperparameters significantly influence algorithm performance.

Interactions between hyperparameters vary across algorithms.

The analysis guides effective hyperparameter tuning strategies.

Abstract

We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interaction effect with other hyperparameters. Using three sensitivity analysis methods, Morris LHS, Morris, and Sobol, to systematically analyze tunable hyperparameters of covariance matrix adaptation evolutionary strategy, differential evolution, non-dominated sorting genetic algorithm III, and multi-objective evolutionary algorithm based on decomposition, the framework reveals the behaviors of hyperparameters to sampling methods and performance metrics. That is, it answers questions like what hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyperparameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.