What type of study is this?

This is a Quantitative Study study.

October 31, 2025Open Access

CoEGAN-BO: Synergistic Co-Evolution of GANs and Bayesian Optimization for High-Dimensional Expensive Many-Objective Problems

Puntos clave

CoEGAN-BO outperforms existing algorithms in many-objective optimization tasks, achieving better convergence.
Performance evaluated on multiple benchmark tasks suggests significant improvements in diversity and efficiency.
The approach integrates generative adversarial networks with a novel co-evolutionary process for optimization tasks.
The findings imply that combining BO with GANs can effectively address challenges in data-scarce environments.

Resumen

Bayesian optimization (BO) struggles with data scarcity and poor scalability in high-dimensional many-objective optimization problems. To address this, we propose Co-Evolutionary GAN–Bayesian Optimization (CoEGAN-BO), a novel framework that synergizes generative adversarial networks (GANs) with Bayesian co-evolutionary search for data-driven optimization. The GAN module generates synthetic samples conditioned on promising regions identified by BO, while a co-evolutionary mechanism maintains two interacting populations: one explores the GAN’s latent space for diversity, and the other exploits BO’s probabilistic model for convergence. A bi-stage infilling strategy further enhances efficiency: early iterations prioritize exploration via Lp-norm-based candidate selection, later switching to a max–min distance criterion for Pareto refinement. Experiments on expensive multi/many-objective benchmarks show that CoEGAN-BO outperforms four state-of-the-art surrogate-assisted algorithms, achieving superior convergence and diversity under limited evaluation budgets.

Leer artículo completoexternamente

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo