用元学习自动发现新质量-多样性算法，无需人工设计竞争规则。
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
- 用注意力神经网络参数化竞争规则，自动演化算法结构。
- 在机器人控制等任务中表现优于或相当现有算法。
- 优化性能时自然保持多样性，揭示多样性是高效优化本质。
质量-多样性算法是一类强大的进化算法，通过模拟生物进化中的局部竞争机制，生成高性能且多样化的解集。尽管这类算法能有效促进多样性和创新，其具体机制仍依赖于启发式规则，如MAP-Elites中的网格竞争或无结构存档中的最近邻竞争。本文提出一种根本性新方法：利用元学习自动发现新型质量-多样性算法。通过采用基于注意力的神经架构参数化竞争规则，我们演化出能捕捉个体间复杂描述空间关系的新算法。所发现算法在性能上达到或超过现有基准，且在更高维度、更大种群规模及机器人控制等分布外领域表现出强泛化能力。值得注意的是，即使仅以适应度为目标优化，这些算法仍自然维持多样性的种群，表明元学习重新发现了多样性对有效优化的根本重要性。
原文摘要 · Abstract (English)
Quality-Diversity has emerged as a powerful family of evolutionary algorithms that generate diverse populations of high-performing solutions by implementing local competition principles inspired by biological evolution. While these algorithms successfully foster diversity and innovation, their specific mechanisms rely on heuristics, such as grid-based competition in MAP-Elites or nearest-neighbor competition in unstructured archives. In this work, we propose a fundamentally different approach: using meta-learning to automatically discover novel Quality-Diversity algorithms. By parameterizing the competition rules using attention-based neural architectures, we evolve new algorithms that capture complex relationships between individuals in the descriptor space. Our discovered algorithms demonstrate competitive or superior performance compared to established Quality-Diversity baselines while exhibiting strong generalization to higher dimensions, larger populations, and out-of-distribution domains like robot control. Notably, even when optimized solely for fitness, these algorithms naturally maintain diverse populations, suggesting meta-learning rediscovers that diversity is fundamental to effective optimization.
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