Meta-Learning Inspired Single-Step Generative Model for Expensive Multitask Optimization Problems

Abstract

In expensive multitask optimization problems (ExMTOPs), multiple complex tasks must be optimized simultaneously under limited computational budgets. Existing approaches, often based on surrogate models, aim to approximate objective functions but struggle to generalize across heterogeneous tasks, depend on task-specific sampling, and require frequent retraining. To address these challenges, we propose the Multifactorial Evolutionary Algorithm–Single Step Generative Model (MFEA-SSG), a meta-learning-inspired framework that learns to generate high-quality solutions across tasks. Inspired by meta-learning, we treat each random shuffle of the decision variables as a unique pseudo-task, training the model on a distribution of these tasks to learn a task-agnostic prior about the structure of elite solutions. This process disrupts task-specific dependencies, allowing the model to learn transferable structures from recomposed samples. We then adopt a diffusion-based generative model to learn the distribution of optimal solutions, enabling knowledge transfer across tasks without directly approximating objective functions. To reduce inference cost, we introduce a student model distilled from the diffusion process. Unlike conventional diffusion models that denoise iteratively, the student generates solutions in a single forward pass, significantly reducing inference time. Comprehensive experiments on both general multitask benchmarks and a real-world protein mutation prediction scenario demonstrate that MFEA-SSG achieves high-quality solutions with fast convergence and low computational cost under limited evaluation budgets, outperforming state-of-the-art general and ExMTOPs algorithms.