HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search

Ruiyu Zhang; Lin Nie; Xin Zhao

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arXiv · 2508.15555

HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search

Ruiyu Zhang·Lin Nie·Xin Zhao

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Abstract

HEAS is a Python framework that connects agent-based simulation, evolutionary search, and scenario-based evaluation in a single reproducible pipeline. It is designed for researchers who study systems where local interactions produce system-level outcomes-ecosystems, organizations, markets, or regulatory environments-and who need to search over candidate strategies and compare them across uncertain scenarios. HEAS combines three modules: a hierarchy runtime for composing simulations from reusable process layers, an evolutionary tuner for single- or multi-objective search backed by DEAP, and a game module for evaluating strategies across scenario ensembles. Its central design principle is the "metric contract": the same outcome function is shared by optimization, evaluation, and validation, so that different parts of an analysis cannot silently rank strategies by different quantities.

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Ruiyu Zhang, Lin Nie, Xin Zhao. 2026-06-20. HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search. https://arxiv.org/abs/2508.15555

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