Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies

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 are hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyper -parameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.

Automated summary

This study conducts a comprehensive sensitivity analysis on the hyperparameters of different optimization algorithms, revealing their influence patterns and interaction effects on algorithm performance, providing guidance for algorithm configuration.