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 multiagent 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：尽管演化计算天然适用于工程领域的自动发现，它同样可用于揭示人类及组织如何提升效能。本文以组织创新搜索这一现实问题为驱动，首先将人类创造性问题求解形式化为竞争性多智能体搜索（CMAS）。CMAS区别于现有单智能体与团队搜索问题的核心在于：智能体通过彼此搜索行为的知识交互，以及由这些搜索引发的搜索地形动态变化产生互动。核心假设是演化计算可用于发现CMAS的有效策略——通过在NK模型（即部分关联且可调节崎岖度的适应性地形）上的系列实验验证该假设。针对不同竞争环境分别演化出专业化策略，同时获得跨环境普适的通用策略。相较于人工设计策略及基于传统树搜索的策略，这些策略展现出更高的效能与复杂性。借助本文提出的该类地形球面可视化新技术，我们得以洞悉成功策略的作用机理（例如通过追踪地形中的正向变化）。由此，本文为未来将人类多种创造性活动视作竞争性多智能体搜索的研究提供了潜在框架。