Learning Semantics-aware Search Operators for Genetic Programming

2025-02-06

AI Summary

To address the rugged fitness landscape and the lack of semantic guidance in traditional syntactic operators for test-driven program synthesis, this paper proposes a semantics-aware genetic programming framework. Methodologically, it (1) employs graph neural networks to model instruction-data interactions within program abstract syntax graphs, generating node saliency maps that quantify semantic importance; (2) introduces the notion of “latent value” to prioritize low-fitness yet evolutionarily promising individuals during search; and (3) designs semantics-sensitive mutation and crossover operators. Evaluated on multiple symbolic regression benchmarks, the approach achieves up to 42% faster convergence compared to classic tree-based genetic programming, while significantly improving solution quality stability. Ablation studies confirm the distinct and measurable contributions of each component.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Programming

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Fitness landscapes in test-based program synthesis are known to be extremely rugged, with even minimal modifications of programs often leading to fundamental changes in their behavior and, consequently, fitness values. Relying on fitness as the only guidance in iterative search algorithms like genetic programming is thus unnecessarily limiting, especially when combined with purely syntactic search operators that are agnostic about their impact on program behavior. In this study, we propose a semantics-aware search operator that steers the search towards candidate programs that are valuable not only actually (high fitness) but also only potentially, i.e. are likely to be turned into high-quality solutions even if their current fitness is low. The key component of the method is a graph neural network that learns to model the interactions between program instructions and processed data, and produces a saliency map over graph nodes that represents possible search decisions. When applied to a suite of symbolic regression benchmarks, the proposed method outperforms conventional tree-based genetic programming and the ablated variant of the method.

Problem

• Improve genetic programming search efficiency
• Introduce semantics-aware search operators
• Enhance program synthesis with graph neural networks

Innovation

• Semantics-aware search operator
• Graph neural network modeling
• Saliency map for search decisions