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While evolutionary computation is well suited for automatic discovery inengineering, it can also be used to gain insight into how humans andorganizations could perform more effectively. Using a real-world problem ofinnovation search in organizations as the motivating example, this articlefirst formalizes human creative problem solving as competitive multi-agentsearch (CMAS). CMAS is different from existing single-agent and team searchproblems in that the agents interact through knowledge of other agents'searches and through the dynamic changes in the search landscape that resultfrom these searches. The main hypothesis is that evolutionary computation canbe used to discover effective strategies for CMAS; this hypothesis is verifiedin a series of experiments on the NK model, i.e.\ partially correlated andtunably rugged fitness landscapes. Different specialized strategies are evolvedfor each different competitive environment, and also general strategies thatperform well across environments. These strategies are more effective and morecomplex than hand-designed strategies and a strategy based on traditional treesearch. Using a novel spherical visualization of such landscapes, insight isgained about how successful strategies work, e.g.\ by tracking positive changesin the landscape. The article thus provides a possible framework for studyingvarious human creative activities as competitive multi-agent search in thefuture.

Evolving Strategies for Competitive Multi-Agent Search