This work introduces an Evolutionary Language Model that searches over natural-language policy descriptions and compiles typed programs for execution and shows that language can serve as a steerable, execution-grounded search representation over executable program space.
Abstract
Program evolution can measure whether a mutation helped, but it rarely controls how far the mutation moves in behavior space. Syntactic edit size is an unreliable proxy: a small code change can alter nearly every action, while a larger rewrite can preserve the same execution trace. We introduce an Evolutionary Language Model that searches over natural-language policy descriptions and compiles typed programs for execution. A fully fine-tuned Qwen3-8B model learns three task-conditioned operations: conditional semantic mutation, natural language to domain-specific language (GPTL) compilation, and GPTL to natural language translation. The model is fine-tuned with conditional input on the mutation strength (low, medium, high) using Direct Preference Optimization (oDPO). Across 252 fixed-budget evolutionary searches, oDPO improves both behavioral calibration and finite-budget search efficiency. Natural-language attains the highest observed held-out fitness. Our analysis shows that the condition input (mutation strength) systematically changes semantic edit composition and that language mutations preserve more parent fitness at matched small-to-moderate behavioral displacement. These results show that language can serve as a steerable, execution-grounded search representation over executable program space.
This work proposes a hybrid framework that integrates Large Language Models into GP in two complementary roles: as a semantic mutation operator that proposes non-local program rewrites during evolution, and as a post-evolution repair that iteratively refines single failed programs after search terminates.
Woletemaryam Liyew, Dojun Oh, Seokki Lee et al.· Proceedings of the Genetic a...· 0 citations
EvoMem is introduced, a persistent memory architecture for LLM-based evolutionary program search that captures and reuses candidate mutation knowledge and provides evidence that persistent memory can reduce some redundant exploration and improve the reuse and adaptation of successful strategies in LLM-driven evolutionary search.
Viktor Volkov, V. Khrulkov, Andrey V. Galichin et al.· 0 citations
Differential compiler testing requires automatically generated programs that are not only diverse and bug-revealing, but also semantically well-defined and reproducible. Rule-based generators provide strong validity guarantees but offer limited control over semantic variation, while large language models (LLMs) can synthesize expressive programs without principled mechanisms for balancing competing testing objectives. This paper proposes LMOEC, a constrained multi-objective evolutionary framework that integrates code language models as semantic genetic operators within an NSGA-II search process. Instead of using the LLM as a one-shot generator, we employ it for population initialization, crossover, and mutation at the program level, enabling semantics-aware recombination while preserving strict admissibility constraints. Compiler test generation is formulated as a multi-objective optimization problem that simultaneously promotes structural diversity, cross-configuration output inconsistency, semantic complexity, and robustness to mutation. A constraint-driven acceptance pipeline enforces syntactic validity, deterministic execution, bounded runtime, and avoidance of undefined behavior before evolutionary selection. By maintaining a Pareto front of non-dominated programs, LMOEC preserves multiple high-value test archetypes reflecting different trade-offs between bug exposure and reproducibility. The framework demonstrates how expressive code models can be systematically embedded into evolutionary multi-objective optimization for reliability-critical software testing.
Lang Hong Nguyet Anh, Ho Viet Duc Luong, Vu Van An· Annual Conference on Genetic...· 0 citations
This project explores the Countdown arithmetic reasoning task: given a set of numbers, produce an arithmetic expression that evaluates to a target value on the Qwen 2.5-0.5B base model and proposes two complementary extensions targeting these failure modes.
Analysis shows that many generated operators use semantics to guide selection, suggesting that LLMs can produce non-trivial search heuristics from the task description alone, and the relationship between public LLM leaderboard rankings and GP performance is examined.
Hengzhe Zhang, Qi Chen, Bing Xue et al.· 1 citation
Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery. However, current approaches are fundamentally constrained by three bottlenecks: structurally blind parent selection, sparse whole-program evaluation rewards, and static mutation operators that fail to adapt during search. We present GAE (Graph-Augmented Evolution), a framework that resolves these limitations through a tightly coupled, three-pillar architecture. First, a relational graph neural network (GNN) parses programs into typed computation graphs, producing structure-aware embeddings. Second, an RL-optimized meta-controller leverages these embeddings to replace blind evolutionary sampling with a directed policy, dynamically selecting optimal parents and mutation directions based on reward history. Third, an online GRPO fine-tuning loop continuously updates the LLM mutation operator at test-time using group-normalized evaluation rewards, directly aligning the model's generation distribution with high-fitness structural edits. We evaluate GAE on a challenging scientific discovery task: symbolic regression for complex nonlinear oscillator systems. By transforming stochastic search into a directed, self-improving trajectory, GAE efficiently discovers closed-form physical equations, consistently matching or outperforming static LLM-driven baselines and achieving state-of-the-art out-of-distribution performance.