Continuous Program Search

2026-02-07

AI Summary

This work addresses the poor locality and low sample efficiency in genetic programming, where minor syntactic mutations often induce drastic behavioral changes. To mitigate this, the authors construct a continuous program space endowed with behavioral meaning and introduce a semantics-aware mutation operator. This operator establishes a behaviorally trustworthy neighborhood through block-decomposition embeddings to quantify locality, and combines flow-model-guided directional sampling with geometric compilation-based mutations within semantically aligned subspaces. Evaluated under identical evolutionary strategies and evaluation budgets, the proposed method discovers superior trading strategies using only one-tenth of the evaluations required by baseline approaches, significantly improving the median out-of-sample Sharpe ratio.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Reasoning under Uncertainty: Probabilistic Programming

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 $(\mu+\lambda)$ 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.

Problem

• Genetic Programming
• Program Locality
• Mutation Operators
• Search Efficiency
• Behavioral Shift

Innovation

• Continuous Program Search
• Geometry-compiled Mutation
• Behavioral Locality
• Semantic Subspace
• Evolutionary Program Synthesis