BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems

Abstract

Novelty search (NS) refers to a class of exploration algorithms that seek to uncover diverse system behaviors through simulations or experiments. Such diversity is central to many AI-driven discovery and design tasks, including material and drug development, neural architecture search, and reinforcement learning. However, existing NS methods typically rely on evolutionary strategies and other meta-heuristics that require dense sampling of the input space, making them impractical for expensive black-box systems. In this work, we introduce BEACON, a sample-efficient, Bayesian optimization-inspired approach to NS that is tailored for settings where the input-to-behavior relationship is opaque and costly to evaluate. BEACON models this mapping using multi-output Gaussian processes (MOGPs) and selects new inputs by maximizing a novelty metric computed from posterior samples of the MOGP, effectively balancing the exploration-exploitation trade-off. By leveraging recent advances in posterior sampling and high-dimensional GP modeling, our method remains scalable to large input spaces and datasets. We evaluate BEACON across ten synthetic benchmarks and eight real-world tasks, including the design of diverse materials for clean energy applications. Our results show that BEACON significantly outperforms existing NS baselines, consistently discovering a broader set of behaviors under tight evaluation budgets.