Large Language Models and Evolutionary Computation

Hitoshi Iba is a Professor at the Graduate School of Information Science and Technology at the University of Tokyo. From 1990 to 1998, he was a senior researcher at the Electro Technical Laboratory (ETL) in Ibaraki, Japan. He is a founding associate editor of the Journal of Genetic Programming and Evolvable Machines (GPEM) and was a founding associate editor of IEEE Transactions on Evolutionary Computation. He has published more than 100 papers and is a (co-)author of more than 20 books. He is also an underwater naturalist and experienced PADI divemaster, having completed about 1,300 dives.João Eduardo Batista is a postdoctoral researcher at RIKEN-CCS, a leading research center in Japan for computational science and high-performance computing. He graduated with a PhD in Informatics from the Faculty of Sciences at the University of Lisbon in 2024, having researched the application of genetic programming for interpretable feature engineering in remote sensing. Currently, his research topics are attribution in LLMs and LLM optimization, as well as high-performance C code optimization using interpretable machine learning techniques.Jinglue Xu is a researcher at Sakana AI, a Tokyo-based artificial intelligence company focused on generative AI and evolutionary computation. He received his Ph.D. in Information Science and Technology from the University of Tokyo in 2025. His research interests include large language models (LLMs), autonomous agents, evolutionary computation, and AutoML. He has conducted multiple research projects exploring the combination of evolutionary computation, LLMs, and AutoML. Currently, he works at Sakana AI on developing more efficient evolutionary computation methods and their applications to LLMs.

• Chapter 1 Introduction.- Chapter 2 Examples of using LLMs as Metaheuristics.- Chapter 3 LLMs for Evolutionary Optimization.- Chapter 4 LLMs for Metaheuristics.- Chapter 5 Towards Scalable, Robust, and Open-ended LLM-EC Integration.- Chapter 6 Conclusion.- Chapter 7 Appendix A: Basic Tools.- Chapter 8 Appendix B: Case Study – LLM for EC Operators.- Chapter 9 Appendix C: Case Study – LLMs for AutoML.