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，其选择结果会影响后续生成结果。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，但需与用户先前选择保持相似，以减少用户疲劳。针对这些问题，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较小区域（窗口）中采样。用户做出选择后，窗口将聚焦于所选个体的行为特征，进化算法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的自适应系统，用户的选择引导搜索向行为空间的特定区域推进。该系统在建筑布局生成这一约束优化任务上进行了测试，通过双存档方法利用质量多样性。基于可控人工用户的实验结果表明，尽管全局探索效果不及MAP-Elites算法显著，但该系统能够找到更符合用户偏好的解决方案。