Genetic Programming yields interpretable programs, but small syntactic mutations can induce large, unpredictable behavioral shifts, degrading locality and sample efficiency. We frame this as an operator-design problem: learn a continuous program space where latent distance has behavioral meaning, then design mutation operators that exploit this structure without changing the evolutionary optimizer. We make locality measurable by tracking action-level divergence under controlled latent perturbations, identifying an empirical trust region for behavior-local continuous variation. Using a compact trading-strategy DSL with four semantic components (long/short entry and exit), we learn a matching block-factorized embedding and compare isotropic Gaussian mutation over the full latent space to geometry-compiled mutation that restricts updates to semantically paired entry--exit subspaces and proposes directions using a learned flow-based model trained on logged mutation outcomes. Under identical $(μ+λ)$ evolution strategies and fixed evaluation budgets across five assets, the learned mutation operator discovers strong strategies using an order of magnitude fewer evaluations and achieves the highest median out-of-sample Sharpe ratio. Although isotropic mutation occasionally attains higher peak performance, geometry-compiled mutation yields faster, more reliable progress, demonstrating that semantically aligned mutation can substantially improve search efficiency without modifying the underlying evolutionary algorithm.

翻译：遗传编程能够生成可解释的程序，但微小的语法突变可能引发巨大且不可预测的行为偏移，从而降低局部性与样本效率。我们将此问题构建为算子设计问题：首先学习一个连续程序空间，使得潜在距离具有行为意义；随后设计能够利用此结构而不改变进化优化器的变异算子。通过追踪受控潜在扰动下的动作级散度，我们使局部性可度量，从而识别出行为局部连续变化的经验信任域。使用一个包含四个语义组件（多头/空头入场与出场）的紧凑交易策略领域专用语言，我们学习与之匹配的块分解嵌入，并比较全潜在空间上的各向同性高斯变异与几何编译变异——后者将更新限制在语义配对的入场-出场子空间，并利用基于记录变异结果训练的流模型生成更新方向。在五种资产上采用相同的$(μ+λ)$进化策略与固定评估预算条件下，学习得到的变异算子能以少一个数量级的评估次数发现强效策略，并获得最高的样本外夏普比率中位数。尽管各向同性变异偶尔能达到更高的峰值性能，但几何编译变异能实现更快、更可靠的进展，这表明语义对齐的变异可在不修改底层进化算法的前提下显著提升搜索效率。