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

arXiv: https://arxiv.org/abs/2207.04820 · HTML: https://mlsanity.com/paper/2207.04820/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 82 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization](https://mlsanity.com/paper/1701.00879/) (2017)
- [Pitfalls and Best Practices in Algorithm Configuration](https://mlsanity.com/paper/1705.06058/) (2017)
- [ACO for Continuous Function Optimization: A Performance Analysis](https://mlsanity.com/paper/1707.01812/) (2017)
- [Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms](https://mlsanity.com/paper/1305.0763/) (2013)
- [Simultaneous Optimization of Neural Network Weights and Active Nodes using Metaheuristics](https://mlsanity.com/paper/1707.01810/) (2017)

## Built on by (most cited first, 3 of 3)

- [Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN](https://mlsanity.com/paper/2412.17629/) (2024)
- [Prεεmpt: Sanitizing Sensitive Prompts for LLMs](https://mlsanity.com/paper/2504.05147/) (2025)
- [Development of a Multiprocessing Interface Genetic Algorithm for Optimising a Multilayer Perceptron for Disease Prediction](https://mlsanity.com/paper/2506.15694/) (2025)

## Abstract (arXiv)

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.