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Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.<ref>{{Cite book |last1=Sarker |first1=R. A. |last2=Ray |first2=T. |chapter=Agent Based Evolutionary Approach: An Introduction |doi=10.1007/978-3-642-13425-8_1 |title=Agent-Based Evolutionary Search |series=Adaptation, Learning, and Optimization |volume=5 |pages=1–11 |year=2010 |isbn=978-3-642-13424-1 }}</ref>
===In team science===
In the realm of team science, agent-based modeling has been utilized to assess the effects of team members' characteristics and biases on team performance across various settings.<ref>{{cite journal |last1=Boroomand |first1=Amin |last2=Smaldino |first2=Paul E. |title=Superiority bias and communication noise can enhance collective problem solving. |journal=Journal of Artificial Societies and Social Simulation |date=2023 |volume=26 |issue=3 |doi=10.18564/jasss.5154|doi-access=free }}</ref> By simulating interactions between agents—each representing individual team members with distinct traits and biases—this modeling approach enables researchers to explore how these factors collectively influence the dynamics and outcomes of team performance. Consequently, agent-based modeling provides a nuanced understanding of team science, facilitating a deeper exploration of the subtleties and variabilities inherent in team-based collaborations.
===In economics and social sciences===
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