Jul 2026· Annual Conference on Genetic and Evolutionary Computation· 0 citations· 27 references
Computer Science
TL;DR
This work proposes a pipeline that introduces a novel regularization architecture balancing performance and complexity, and mitigate the side effects of automated tuning through two novel components: a symbolic pruning mutator and a complexity-aware mutation gate that explicitly filters out mutations leading to excessive code growth.
Abstract
Designing effective heuristics remains a labor-intensive task traditionally reserved for domain experts. While Large Language Model (LLM)-driven evolutionary search offers a path toward automated discovery, existing methods often suffer from slow convergence, primarily due to inefficient hyperparameter tuning. Delegating tuning to specialized optimizers improves performance, but it comes at the cost of code bloat and overfitting. To address these issues, we propose a pipeline that introduces a novel regularization architecture balancing performance and complexity. Specifically, we mitigate the side effects of automated tuning through two novel components: (i) symbolic pruning mutator, which combines LLM semantic guidance with Abstract Syntax Tree analysis to eliminate algorithmic redundancy; and (ii) a complexity-aware mutation gate that explicitly filters out mutations leading to excessive code growth. Our framework substantially accelerates convergence and improves generalization on standard benchmarks of the 3D Single Container Loading Problem. The discovered heuristics match state-of-the-art human designed algorithms and rediscover similar geometric principles used by experts, highlighting the framework's ability to autonomously extract meaningful domain knowledge.
A framework that combines large language models (LLMs) for problem understanding with a structured Biased Random-Key Genetic Algorithm (BRKGA) configurator for algorithm realization is presented, allowing users to describe optimization problems in natural language and receive executable GPU-accelerated GA implementations.
Harishjitu Seesandrn, M. Sodhi, Resit Sendag· Proceedings of the Genetic a...· 0 citations
This work proposes a general LES framework that incorporates feature-assisted niche construction within abstract search spaces, enabling the seamless integration of niche-based search strategies from evolutionary computation and introduces PartEvo (Partition to Evolve), an LES method that combines niche collaborative search and advanced prompting strategies to improve algorithm discovery efficiency.
Qinglong Hu, Qingfu Zhang· Neural Information Processin...· 10 citations· ⚡3
LLM-based automated heuristic design (AHD) typically scores executable programs on complete instances or within fixed solver components. In large-scale routing problems, localized reconstruction reduces the size of each optimization task, but repair regions within the same incumbent can exhibit substantially different structures. One construction rule must therefore compromise across them. In this paper, we propose SpecAHD, a coupled bilevel framework for within-instance specialization. An upper-level search learns where to expose bounded repair regions, while a lower-level search evolves a complementary repertoire of executable heuristics for the induced repair tasks. The upper-level program determines the repair tasks seen by the lower level, while checked repair outcomes determine how upper-level programs are evaluated. The lower-level objective favors heuristics that perform well on average or solve tasks that the current repertoire handles poorly. For the repair tasks induced by a fixed upper-level program and a fixed lower-level candidate pool, this objective is monotone submodular, allowing greedy repertoire selection with a (1-1/e) approximation guarantee. Across four routing problems and multiple LLM backbones, SpecAHD reduces held-out objective cost by up to 57.7% against the strongest competing AHD baseline and outperforms the per-instance baseline envelope on most public instances.
Large Language Models (LLMs) are opening new directions for automated heuristic design (AHD), allowing evolutionary methods to create and enhance heuristics for constrained optimization problems (COPs). However, most existing approaches face the challenge of the exploration-exploitation balance, where the evolution needs to escape convergence to homogeneous populations and discover as large a heuristic landscape as possible. To address this challenge, we introduce Quality-Diversity Evolution (QDEvo), a multi-objective framework that integrates Quality-Diversity optimization with LLM-based AHD. At its core, QDEvo employs a semantic survival selection mechanism that clusters algorithms by functional similarity, then applies local Pareto competition. Evaluation on well-known COPs benchmarks and real-world problems shows that our method consistently outperforms the state-of-the-art baseline in both Hypervolume and Inverted Generational Distance metrics. These results facilitate further exploration of the algorithmic design space, while ensuring competitive solution quality and efficiency.
Nam Do Khanh, Nhat Nguyen Tran Minh, Dat Pham Vu Tuan et al.· Proceedings of the Genetic a...· 1 citation
A Directed Graph-Guided Automated Algorithm Design framework, termed DGA$_2$D, which structures the open-ended program space as a directed graph, where each node represents a functional operator that can be instantiated using one of multiple candidate code implementations, while directed walks constitute complete algorithmic pipelines.
Jiale Zhao, Zimu Chen, Sirui Mao et al.· 0 citations
Container images are fundamental to cloud deployment, with their build instructions (e.g., Dockerfiles) critically impacting the efficiency and stability of cloud service. Manually authoring these instructions is error-prone, while Large Language Models (LLMs) lack the domain knowledge to generate both correct and optimized Dockerfiles reliably. This problem may cause runtime failures, prolonged deployment times and increased storage overhead. This paper introduces a novel knowledgeenhanced approach to automate Dockerfile generation. First, we construct a Dockerfile Instructions Knowledge Graph (DIKG) by analyzing a large corpus, capturing complex dependencies among images, packages, and commands. Leveraging DIKG, we design DKRAG, a retrieval-augmented generation system that guides an LLM to interpret user requirements and produce semantically accurate instructions. The output is further optimized via log-based repair and static dependency-aware refactoring for correctness, layer sharing, and minimal image size. Comprehensive experiments show our approach significantly improves the generation accuracy while also reducing build time and storage overhead compared to state-of-the-art methods.
Kun Wang, Yao Wu, Hao Fan et al.· Fall Joint Computer Conferen...· 0 citations