Meta-Black-Box Optimization with Bi-Space Landscape Analysis and Dual-Control Mechanism for SAEA

TL;DR

This paper introduces DB-SAEA, a novel meta-optimization framework that employs dual control and bi-space landscape analysis to enhance surrogate-assisted evolutionary algorithms for multi-objective black-box problems, demonstrating superior performance and transferability.

Contribution

The paper presents the first MetaBBO framework with dual-level control and bi-space analysis, improving flexibility, scalability, and transferability of SAEAs across diverse multi-objective tasks.

Findings

Outperforms state-of-the-art baselines on various benchmarks.

Exhibits strong zero-shot transfer to unseen, higher-dimensional tasks.

Utilizes a bi-space ELA with attention architecture for scalable landscape analysis.

Abstract

Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm (DB-SAEA), a Meta-Black-Box Optimization (MetaBBO) framework tailored for multi-objective problems. DB-SAEA learns a meta-policy that jointly regulates candidate generation and infill criterion selection, enabling dual control. The bi-space Exploratory Landscape Analysis (ELA) module in DB-SAEA adopts an attention-based architecture to capture optimization states from both true and surrogate evaluation spaces, while ensuring scalability across problem dimensions, population sizes, and objectives. Additionally, we integrate TabPFN as the surrogate model for accurate and efficient prediction with uncertainty estimation. The framework is trained via reinforcement learning, leveraging parallel sampling and centralized training to enhance efficiency and transferability across tasks. Experimental results demonstrate that DB-SAEA not only outperforms state-of-the-art baselines across diverse benchmarks, but also exhibits strong zero-shot transfer to unseen tasks with higher-dimensional settings. This work introduces the first MetaBBO framework with dual-level control over SAEAs and a bi-space ELA that captures surrogate model information.