AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This thesis contributes a methodology for evaluating prompt operators through isolated parent-child comparisons, enabling their behaviour to be characterized independently of complete evolutionary runs.
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.
EvoHIIT, an LLM-assisted evolutionary framework for the design of High-Intensity Interval Training (HIIT) programs, is introduced, and preference-based selection mechanisms are studied to provide empirical insight into human-aligned evolution of natural language solutions.
Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems. However, existing methods primarily optimize a single heuristic, whereas practical optimization frameworks often rely on multiple interacting components. Directly extending single-heuristic methods is challenging because early component selection can overlook components with late potential, while independent evolution ignores inter-component dependencies. We propose MuEvo, an LLM-driven framework for evolving heuristic ensembles under ensemble-level feedback. MuEvo combines Dynamic Component Management, which uses short-budget probing and a reversible lifecycle to revise component priorities throughout the search, with LLM-Driven Co-Evolution, which coordinates component populations through Multi-Ensemble Evaluation, Cross-Component Information Sharing, Relation-Guided Pair Evolution, and Adaptive Budget Allocation. We evaluate MuEvo on selection hyper-heuristics and componentized ant colony optimization across four combinatorial optimization domains. Results show that MuEvo consistently improves human-designed frameworks and outperforms representative multi-component extensions of state-of-the-art LLM-AHD methods, demonstrating its effectiveness across both controller-mediated heuristic pools and functionally differentiated algorithmic components.
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.
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.
It is found that none of the LLaMEA generated algorithms outperformed KMeans++, and prompting strategy affects the structural diversity of generated algorithms, with exemplar-injection producing the broadest range of algorithm families.
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