arXiv (All)
open access
GA-Agent: Large Language Models as Hyperparameter Optimizers for Evolutionary Controller Synthesis
Tuning PID controllers to satisfy competing objectives - low tracking error, fast settling, limited overshoot, and moderate control effort - is labor-intensive and requires expertise. Genetic algorithms (GAs) offer gradient-free optimization of controller gains against a weighted fitness function, b
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Deep Generative Models for Complex Multi-component Industrial System Generation#395403
- SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment#632761
- Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problems#974359
- Robust error-minimization in the genetic code across physicochemical metrics and variant codes: a graph-theoretic analysis in GF(2)6#234310
- RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood#238630
Record · ID 1064269
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive