Quality Diversity (QD) algorithms have been proposed to search for a large collection of both diverse and high-performing solutions instead of a single set of local optima. While early QD algorithms view the objective and descriptor functions as black-box functions, novel tools have been introduced to use gradient information to accelerate the search and improve overall performance of those algorithms over continuous input spaces. However a broad range of applications involve discrete spaces, such as drug discovery or image generation. Exploring those spaces is challenging as they are combinatorially large and gradients cannot be used in the same manner as in continuous spaces. We introduce map-elites with a Gradient-Informed Discrete Emitter (ME-GIDE), which extends QD optimisation with differentiable functions over discrete search spaces. ME-GIDE leverages the gradient information of the objective and descriptor functions with respect to its discrete inputs to propose gradient-informed updates that guide the search towards a diverse set of high quality solutions. We evaluate our method on challenging benchmarks including protein design and discrete latent space illumination and find that our method outperforms state-of-the-art QD algorithms in all benchmarks.

翻译：质量多样性（QD）算法旨在搜索大量兼具多样性和高性能的解，而非单一局部最优解集。早期QD算法将目标函数和描述符函数视为黑箱函数，而新型工具已引入梯度信息来加速连续输入空间上的搜索并提升算法整体性能。然而，药物发现或图像生成等广泛应用涉及离散空间。由于这些空间组合规模庞大且梯度无法像连续空间那样直接使用，探索此类空间极具挑战。我们提出融合梯度信息离散发射器的地图精英算法（ME-GIDE），该算法将可微函数上的QD优化扩展至离散搜索空间。ME-GIDE利用目标函数和描述符函数关于离散输入的梯度信息，提出基于梯度信息的更新策略，引导搜索向多样化的高质量解集收敛。我们在蛋白质设计、离散潜空间照明等挑战性基准测试中评估该方法，发现该方法在所有基准测试中均优于最先进的QD算法。