arXiv AI
Jul 13

LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms

arXiv:2607. 08791v1 Announce Type: cross Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise.

By Georgios Laskaris, Reuben Brasher, Niki van Stein, Elena Raponi, Thomas B\"ack, Florian Neukart
arXiv AI
Sep 24

Combining LLMs and Genetic Search for ARC-AGI-2

The paper presents a hybrid approach that combines large language models (LLMs) with genetic algorithms to solve ARC-AGI-2 tasks. An LLM (Qwen3.5-4B) first generates a small set of programs, which seed a genetic algorithm that evolves these programs within a domain‑specific language ensuring validity. This method yields 6 correct solutions out of 60 tasks (10%), outperforming either technique alone.

By Val Dyachenko
arXiv Computation and Language
Sep 1

Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems

arXiv:2505.15741v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm d...

By Dikshit Chauhan, Bapi Dutta, Indu Bala, Niki van Stein, Thomas B\"ack, Anupam Yadav