构建可复现的多目标策略搜索框架，统一评估与优化标准。
HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search
- 分层模块化架构，支持可复用流程组合
- 基于DEAP的进化搜索，支持多目标优化
- 统一指标契约，确保评估一致性
HEAS 是一个 Python 框架，将基于代理的仿真、进化搜索和基于情景的评估整合为单一可复现的流程。适用于研究局部交互产生系统级结果的系统——如生态系统、组织、市场或监管环境——并需在不确定情景下搜索候选策略并进行比较的研究者。该框架包含三个模块：用于从可复用过程层构建仿真的分层运行时、基于 DEAP 的单/多目标进化调优器，以及用于在情景集合中评估策略的游戏模块。其核心设计原则是“指标契约”：优化、评估与验证共享同一结果函数，防止分析各环节隐式使用不同度量对策略排序。
原文摘要 · Abstract (English)
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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