发表机构
New York University; University of Malta（纽约大学; 马耳他大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出LVPG框架，将进化的时间价值形式化，通过长视野信用分配加速有限预算搜索，提升了自动交易策略发现的性能。
AI 中文摘要
在进化搜索中，弱小的子代可能成为能通向高适应度区域的宝贵祖先；即时回报控制机制对这种延迟效用视而不见，仅通过突变的直接后代对突变进行惩罚，即便突变能开辟富有成效的后续谱系。我们在有限时间马尔可夫决策过程中将这一隐藏动态形式化为进化的时间价值。为利用该价值，我们提出谱系价值策略梯度（Lineage-Value Policy Gradients，LVPG），这是一种用于自动交易策略发现的长视野Actor-Critic框架。我们的架构将搜索控制解耦为共享生成主干上的专用策略头：引导式评论家头从多步突变树估计有限时间谱系潜力的价值，而演员头则在剩余搜索预算内动态调节突变强度。我们在匹配的算子、谱系监督、折叠、随机种子和预算下，通过90组配对实验，分离了长视野信用分配对即时回报优化的影响。基于路径的信用分配大幅加速了有限预算搜索，使验证集当前最佳AUC提升了0.394夏普单位；LVPG产生的临时回归比即时回报优化更少，且能更频繁地从回归中恢复。有限时间谱系价值在相同资源约束下实现了更具选择性的非单调搜索和更强的策略。
英文摘要
In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable. Immediate-return control is blind to this delayed utility, penalizing mutations through their immediate offspring even when they open productive future lineages. We formalize this hidden dynamic as the time value of evolution within a finite-horizon Markov decision process. To exploit it, we introduce Lineage-Value Policy Gradients (LVPG), a long-horizon actor-critic framework for automated trading policy discovery. Our architecture decouples search control into specialized policy heads over a shared generative backbone: a bootstrapped critic head estimates the value of finite-horizon lineage potential from multi-step mutation trees, while an actor head dynamically modulates mutation intensity over the remaining search budget. We isolate the impact of long-horizon credit assignment against immediate-return optimization across 90 paired runs under matched operators, lineage supervision, folds, seeds, and budgets. Path-based credit assignment substantially accelerates finite-budget search, increasing validation best-so-far AUC by 0.394 Sharpe units. LVPG also produces fewer temporary regressions than immediate-return optimization and recovers from them more often. Finite-horizon lineage value yields more selective non-monotonic search and stronger policies within identical resource constraints.
CommentsSubmitted to AAAI 2026, 8 pages, 5 figures, 2 tables
