# Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

arXiv: https://arxiv.org/abs/1910.04966 · HTML: https://mlsanity.com/paper/1910.04966/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [Generative Models in Decision Making: A Survey](https://mlsanity.com/paper/2502.17100/) (2025)
- [Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning](https://mlsanity.com/paper/2209.02428/) (2022)
- [A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization](https://mlsanity.com/paper/2206.11526/) (2022)
- [EvoGAN: An Evolutionary Computation Assisted GAN](https://mlsanity.com/paper/2110.11583/) (2021)
- [Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art](https://mlsanity.com/paper/2412.01566/) (2024)
- [ACEvo: Adversarial Co-Evolution of Problem Distributions and Solvers for Combinatorial Optimization](https://mlsanity.com/paper/2506.02594/) (2025)
- [Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search](https://mlsanity.com/paper/2302.02565/) (2023)
- [OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution](https://mlsanity.com/paper/2102.03888/) (2021)
- [Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling](https://mlsanity.com/paper/2402.14600/) (2024)
- [Augmenting High-dimensional Nonlinear Optimization with Conditional GANs](https://mlsanity.com/paper/2103.04748/) (2021)
- [Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search](https://mlsanity.com/paper/2304.04071/) (2023)
- [Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems](https://mlsanity.com/paper/2304.09444/) (2023)
- [Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model](https://mlsanity.com/paper/2108.04197/) (2021)
- [Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach](https://mlsanity.com/paper/2304.04067/) (2023)

## Abstract (arXiv)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.