Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization

• URL: http://arxiv.org/abs/2502.02190v1
• Date: Tue, 04 Feb 2025 10:13:13 GMT
• Title: Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
• Authors: Maxence Faldor, Robert Tjarko Lange, Antoine Cully,
• Abstract summary: Quality-Diversity is a family of evolutionary algorithms that generate diverse populations of high-performing solutions. We propose using meta-learning to automatically discover novel Quality-Diversity algorithms.
• Score: 8.5083347559272
• License:
• Abstract: 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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