While evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively. Using a real-world problem of innovation search in organizations as the motivating example, this article first formalizes human creative problem solving as competitive multi-agent search (CMAS). CMAS is different from existing single-agent and team search problems in that the agents interact through knowledge of other agents' searches and through the dynamic changes in the search landscape that result from these searches. The main hypothesis is that evolutionary computation can be used to discover effective strategies for CMAS; this hypothesis is verified in a series of experiments on the NK model, i.e.\ partially correlated and tunably rugged fitness landscapes. Different specialized strategies are evolved for each different competitive environment, and also general strategies that perform well across environments. These strategies are more effective and more complex than hand-designed strategies and a strategy based on traditional tree search. Using a novel spherical visualization of such landscapes, insight is gained about how successful strategies work, e.g.\ by tracking positive changes in the landscape. The article thus provides a possible framework for studying various human creative activities as competitive multi-agent search in the future.

翻译：虽然进化计算非常适合工程领域的自动发现，但它也可用于深入了解人类和组织如何能更高效运作。本文以组织中创新搜索这一真实世界问题为驱动案例，首先将人类创造性问题求解形式化为多主体竞争搜索（CMAS）。与现有单主体和团队搜索问题不同，CMAS 中主体通过感知其他主体的搜索行为以及由此引发的搜索空间动态变化来进行交互。核心假设是：进化计算可用于发现 CMAS 的有效策略；这一假设通过基于 NK 模型（即部分关联且可调整崎岖度的适应度景观）的一系列实验得到验证。针对不同竞争环境分别进化出专业策略，同时进化出跨环境均表现良好的通用策略。这些策略相比人工设计策略及基于传统树搜索的策略更高效且更复杂。利用新颖的球形景观可视化方法，我们得以洞悉成功策略的工作机制（例如通过追踪景观中的正向变化）。本文因此为未来将各类人类创造性活动研究为多主体竞争搜索提供了可能的框架。