# Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis

arXiv: https://arxiv.org/abs/2403.14413 · HTML: https://mlsanity.com/paper/2403.14413/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Maximizing acquisition functions for Bayesian optimization](https://mlsanity.com/paper/1805.10196/) (2018)
- [HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation](https://mlsanity.com/paper/2012.03826/) (2020)
- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization](https://mlsanity.com/paper/1701.00879/) (2017)
- [The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights](https://mlsanity.com/paper/2203.02433/) (2022)
- [Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer](https://mlsanity.com/paper/2305.03463/) (2023)
- [Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization](https://mlsanity.com/paper/2309.11994/) (2023)

## Built on by (most cited first, 4 of 4)

- [Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems](https://mlsanity.com/paper/2505.15741/) (2025)
- [Automated Evolutionary Optimization for Resource-Efficient Neural Network Training](https://mlsanity.com/paper/2510.09566/) (2025)
- [Relation-Aware Bayesian Optimization of DBMS Configurations Guided by Affinity Scores](https://mlsanity.com/paper/2510.27145/) (2025)
- [Deep learning-guided evolutionary optimization for protein design](https://mlsanity.com/paper/2603.02753/) (2026)

## Abstract (arXiv)

Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for simulations. Bayesian Optimization (BO) and Surrogate-Assisted Evolutionary Algorithm (SAEA) are two widely used gradient-free optimization techniques employed to address such challenges. Both approaches follow a similar iterative procedure that relies on surrogate models to guide the search process. This paper aims to elucidate the similarities and differences in the utilization of model uncertainty between these two methods, as well as the impact of model inaccuracies on algorithmic performance. A novel model-assisted strategy is introduced, which utilizes unevaluated solutions to generate offspring, leveraging the population-based search capabilities of evolutionary algorithm to enhance the effectiveness of model-assisted optimization. Experimental results demonstrate that the proposed approach outperforms mainstream Bayesian optimization algorithms in terms of accuracy and efficiency.