Large Language Models and Evolutionary Computation: Generative AI for Meta-Heuristics
暫譯: 大型語言模型與進化計算：生成式人工智慧於元啟發式演算法的應用

Iba, Hitoshi, Batista, João E., Xu, Jinglue

Large Language Models and Evolutionary Computation: Generative AI for Meta-Heuristics
  • 出版商: Springer
  • 出版日期: 2026-07-14
  • 售價: $5,140
  • 貴賓價: 9.5 折 $4,883
  • 語言: 英文
  • 頁數: 303
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819585961
  • ISBN-13: 9789819585960
  • 相關分類: Large language model

    • 海外代購書籍(需單獨結帳)

商品描述

This book provides theoretical and practical knowledge of an LLM (Large Language Model)-based approach to metaheuristics. In this book, the basic theory and the latest techniques are explained in an easy-to-understand manner, with concrete examples. Another emphasis is its real-world applicability. The book presents empirical examples from practical data and show that the proposed approaches are successful when addressing tasks from the recent research areas such as (1) LLMs for EC (Evolutionary computation), (2) training LLMs for EC, (3) automated machine learning, and (4) program synthesis, etc., details of which will be provided in the appendix for the sake of readers' study. These materials will include a description of available resources for readers interested in gaining hands-on experience with the subject. The fundamental themes of this book, therefore, include recent research on the promising combination of Generative AI, LLMs, evolutionary computation, and metaheuristics. The ultimate goal of this book is to enable readers to apply these ideas to artificial intelligence on their own.
This book is intended for beginners interested in artificial intelligence and artificial life (from undergraduate to graduate students), researchers in related fields, and engineers considering their applications. Therefore, most topics in this book begin with accessible subjects that require no specialized knowledge, though some connect to unsolved problems and cutting-edge research themes.

商品描述(中文翻譯)

這本書提供基於大型語言模型（LLM, Large Language Model）的方法論和實踐知識，專注於元啟發式演算法。書中以易於理解的方式解釋基本理論和最新技術，並提供具體範例。另一個重點是其在現實世界中的應用性。書中展示了來自實際數據的實證範例，並顯示所提出的方法在解決近期研究領域的任務時是成功的，例如（1）用於進化計算（EC, Evolutionary Computation）的LLM，（2）為進化計算訓練LLM，（3）自動化機器學習，以及（4）程式合成等，詳細內容將在附錄中提供，以便讀者學習。這些材料將包括對有興趣獲得實作經驗的讀者可用資源的描述。因此，本書的基本主題包括關於生成式人工智慧、LLM、進化計算和元啟發式演算法的最新研究。本書的最終目標是使讀者能夠獨立將這些理念應用於人工智慧。

本書適合對人工智慧和人工生命感興趣的初學者（從本科生到研究生）、相關領域的研究人員以及考慮其應用的工程師。因此，本書中的大多數主題從不需要專業知識的可接觸主題開始，儘管有些主題與未解決的問題和前沿研究主題相關。

作者簡介

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.

作者簡介(中文翻譯)

伊場仁志是東京大學資訊科學與技術研究所的教授。從1990年到1998年，他在日本茨城的電氣技術研究所（ETL）擔任高級研究員。他是《遺傳編程與可演化機器期刊》（Journal of Genetic Programming and Evolvable Machines, GPEM）的創始副編輯，並曾擔任IEEE《演化計算期刊》的創始副編輯。他已發表超過100篇論文，並且是超過20本書籍的（共同）作者。他也是一名水下自然學家和經驗豐富的PADI潛水長，完成了約1,300次潛水。

若昂·愛德華多·巴蒂斯塔是RIKEN-CCS的博士後研究員，該中心是日本領先的計算科學和高效能計算研究機構。他於2024年在里斯本大學科學學院獲得資訊學博士學位，研究主題為遺傳編程在遙感中的可解釋特徵工程應用。目前，他的研究主題包括大型語言模型（LLMs）的歸因和LLM優化，以及使用可解釋機器學習技術的高效能C語言代碼優化。

徐景略是位於東京的人工智慧公司Sakana AI的研究員，該公司專注於生成式AI和演化計算。他於2025年在東京大學獲得資訊科學與技術博士學位。他的研究興趣包括大型語言模型（LLMs）、自主代理、演化計算和自動機器學習（AutoML）。他進行了多個研究項目，探索演化計算、LLMs和AutoML的結合。目前，他在Sakana AI工作，致力於開發更高效的演化計算方法及其在LLMs中的應用。

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