This paper introduces a user-driven evolutionary algorithm based on Quality Diversity (QD) search. During a design session, the user iteratively selects among presented alternatives and their selections affect the upcoming results. We aim to address two major concerns of interactive evolution: (a) the user must be presented with few alternatives, to reduce cognitive load; (b) presented alternatives should be diverse but similar to the previous user selection, to reduce user fatigue. To address these concerns, we implement a variation of the MAP-Elites algorithm where the presented alternatives are sampled from a small region (window) of the behavioral space. After a user selection, the window is centered on the selected individual's behavior characterization, evolution selects parents from within this window to produce offspring, and new alternatives are sampled. Essentially we define an adaptive system of local QD, where the user's selections guide the search towards specific regions of the behavioral space. The system is tested on the generation of architectural layouts, a constrained optimization task, leveraging QD through a two-archive approach. Results show that while global exploration is not as pronounced as in MAP-Elites, the system finds more appropriate solutions to the user's taste, based on experiments with controllable artificial users.

翻译：本文提出一种基于质量多样性搜索的用户驱动进化算法。在设计过程中，用户迭代地从呈现的备选方案中进行选择，其选择会影响后续结果。我们旨在解决交互式进化的两个主要问题：(a) 必须向用户呈现少量备选方案，以降低认知负荷；(b) 呈现的备选方案应具有多样性，但需与用户上一次选择相似，以减少用户疲劳。为解决这些问题，我们实现了一种MAP-Elites算法的变体，其中备选方案从行为空间的小区域（窗口）中采样。用户做出选择后，窗口中心将定位在所选个体行为表征上，进化过程从该窗口内选择父代个体以产生子代，并采样新的备选方案。本质上，我们定义了一个局部质量多样性自适应系统，用户的选择引导搜索向行为空间的特定区域聚焦。该系统通过双存档方法利用质量多样性，在建筑布局生成这一约束优化任务上进行了测试。结果表明，尽管全局探索能力不如MAP-Elites算法显著，但基于可控人工用户的实验显示，该系统能生成更符合用户品味的解决方案。