A gradient-descent-like learning-based framework in surrogate-assisted evolutionary algorithms for expensive many-objective optimization
Abstract Surrogate-assisted evolutionary algorithms (SAEAs) commonly depend on traditional offspring generation methods such as simulated binary crossover and polynomial mutation, which often lead to suboptimal search efficiencies. This paper introduces the gradient-descent-like learning-based SAEA...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01955-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|