自进化代码代理自动优化汽车风阻预测流程，提升效率与可靠性。
A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction
- 以程序约束优化替代静态模型，结合演化算法与反馈机制生成可执行代理流水线。
- 最佳系统综合得分0.9335，符号准确率达0.9180，适应性采样与岛屿迁移是关键驱动力。
- 支持高通量设计探索与可信升级，适合需要高效迭代与合规性的工程团队。
高保真车辆风阻评估受限于流程摩擦：几何清理、网格重试、队列竞争和跨团队复现失败。本文提出一种以契约为核心的自进化代码代理蓝图，用于在工业约束下发现可执行的风阻系数 $C_d$ 预测代理流水线。方法将代理发现建模为程序的约束优化问题，而非静态模型实例，融合 Famou-Agent 式评估反馈与基于种群的岛屿演化，采用结构化变异（数据、模型、损失、划分策略），并通过多目标选择平衡排序质量、稳定性与成本。硬性评估契约强制防止信息泄露、保证确定性回放、多种子鲁棒性及资源预算，在八种匿名演化算子中，最优系统达到综合得分0.9335，符号准确率0.9180。轨迹与消融分析表明，自适应采样与岛屿迁移是收敛质量的主要驱动因素。部署模式为“筛选-升级”：代理用于高通量设计探索，低置信或分布外案例自动升级至高保真CFD。成果提供可审计、可复用的工作流，加速气动设计迭代，同时保障决策级可靠性、治理可追溯性与安全边界。
原文摘要 · Abstract (English)
High-fidelity vehicle drag evaluation is constrained less by solver runtime than by workflow friction: geometry cleanup, meshing retries, queue contention, and reproducibility failures across teams. We present a contract-centric blueprint for self-evolving coding agents that discover executable surrogate pipelines for predicting drag coefficient $C_d$ under industrial constraints. The method formulates surrogate discovery as constrained optimization over programs, not static model instances, and combines Famou-Agent-style evaluator feedback with population-based island evolution, structured mutations (data, model, loss, and split policies), and multi-objective selection balancing ranking quality, stability, and cost. A hard evaluation contract enforces leakage prevention, deterministic replay, multi-seed robustness, and resource budgets before any candidate is admitted. Across eight anonymized evolutionary operators, the best system reaches a Combined Score of 0.9335 with sign-accuracy 0.9180, while trajectory and ablation analyses show that adaptive sampling and island migration are primary drivers of convergence quality. The deployment model is explicitly ``screen-and-escalate'': surrogates provide high-throughput ranking for design exploration, but low-confidence or out-of-distribution cases are automatically escalated to high-fidelity CFD. The resulting contribution is an auditable, reusable workflow for accelerating aerodynamic design iteration while preserving decision-grade reliability, governance traceability, and safety boundaries.
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