AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
V ENDI E VOLVE augments LLM-guided evolutionary search with label-free distributional diversity signals that diagnose population-level collapse and keep semantically distinct programs influential as context for future LLM mutations.
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 evolutiona...
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 nee...
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 s...
Ide Search is proposed, a framework that systematically integrates a dynamic"Idea Bank" into Tree Search, a framework that systematically integrates a dynamic bank of ideas into Tree Search, and reliably breaks the plateau of a strong pure Tree Search baseline.
Combinatorial problems appear in numerous industrial applications. A common approach is to formulate these problems as declarative constraint models that can subsequently be compiled to and solved by a range of back-end solvers. Recent work shows that Large Language Models (LLMs) can produce correct models from natural...
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.
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