HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search

We developed HEAS (Hierarchical Evolutionary Agent Simulation) as a framework to combine layered agent-based modeling with evolutionary optimization. In HEAS, models are expressed as modular streams scheduled in layers that read and write to a shared context, making cross-scale feedbacks from environment to groups, agents, and aggregators explicit and transparent. With a simple API (simulate, optimize, evaluate), HEAS supports multi-objective search such as NSGA-II, reproducible tournament evaluation, and PyTorch integration for policy learning, enabling researchers to build, compare, and extend simulations across ecological, organizational, and policy domains.

You can apply HEAS directly from PyPI:

pip install heas

For full documentation and tutorials, visit the HEAS project page. The SSRN version is available here, and the interactive demo is available at https://ryzhanghason.github.io/heas/.

Citation as: Ruiyu ZHANG, Lin Nie, and Xin Zhao. (2025). "HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search." Preprint. arXiv:2508.15555. https://arxiv.org/abs/2508.15555