Advanced Machine Learning with Evolutionary and Metaheuristic Techniques
暫譯: 進階機器學習：演化與元啟發技術

Valadi, Jayaraman, Singh, Krishna Pratap, Ojha, Muneendra

Advanced Machine Learning with Evolutionary and Metaheuristic Techniques
  • 出版商: Springer
  • 出版日期: 2025-04-23
  • 售價: $7,070
  • 貴賓價: 9.5 折 $6,717
  • 語言: 英文
  • 頁數: 362
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9819997208
  • ISBN-13: 9789819997206
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning.

It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization and machine learning, paving the way for pioneering innovations in the field.

商品描述(中文翻譯)

本書深入探討進化演算法和元啟發式演算法的實際應用，以提升機器學習的能力。讀者可以了解數據驅動的進化優化在材料力學中的能力，並優化您的學習演算法以達到最佳效率。或者解鎖超參數優化背後的策略，以增強您的遷移學習演算法，從而產生卓越的結果。或者展開一段啟發性的旅程，探索為構建深度學習框架而設計的進化技術。本書還介紹了一個針對物聯網網絡量身定制的智能 RPL 攻擊檢測系統。通過將粒子群優化（Particle Swarm Optimization）與強化學習（Reinforcement Learning）相結合，探索一條有前景的優化途徑。

本書揭示了元啟發式演算法在監督式機器學習演算法中的不可或缺的角色。最終，本書橋接了進化動態優化和機器學習的領域，為該領域的開創性創新鋪平道路。

作者簡介

Dr. Jayaraman Valadi is a Distinguished Professor of Computer Science at FLAME University, Pune, India. He earned his Doctorate degree in Chemistry from Pune University. His research encompasses diverse areas, focusing on modeling and simulations in chemical and biochemical engineering, as well as process modeling, control, and optimization. Over the past decade, he has dedicated his efforts to exploring applications of Machine Learning and Artificial intelligence across various domains. He has dozens of publications in various reputed international journals. Beginning his journey in 1976, Dr. Valadi was associated with the Council of Industrial and Scientific Research (CSIR) in India, where he worked for 33 years and retired as a Deputy Director in 2009. After that, he was a CSIR Emeritus Scientist at the Center for Development of Advanced Computing, Pune till January 2013 & thereafter as a visiting faculty at Shiv Nadar University, Greater Noida, India until May 2023.

Dr. Krishna Pratap Singh is an Associate Professor in the Department of Information Technology at the Indian Institute of Information Technology Allahabad (IIITA), India, where he also heads the Machine Learning and Optimization (MLO) Lab. Dr. Singh earned his Ph.D. in Optimization (2009) from IIT Roorkee, and has over 15 years of research and academic experience. He is a member of the Sakura Science Club, Japan, Senior member IEEE and ACM Member. Currently, his research group is working on Transfer Learning for low resources data and towards developing a model in a Federated learning setting.

Dr. Muneendra Ojha is an Assistant Professor in the Department of Information Technology at the Indian Institute of Information Technology Allahabad (IIITA), India, and leading the Artificial Intelligence and Multiagent Systems (AIMS) lab. Dr. Ojha earned his Ph.D. from IIITA and MS from the University of Missouri-Columbia, USA.Dr. Ojha has more than 19 years of academic and industry experience. His research interests include multi-objective optimization, evolutionary algorithms, semantic web, natural language processing, deep reinforcement learning, and multi-agent systems.

Dr. Patrick Siarry received the PhD degree from the University Paris 6, in 1986 and the Doctorate of Sciences(Habilitation) from the University of Paris 11, in 1994. He was first involved in the development of analog and digital models of nuclear power plants at Electricité de France (EDF. Since 1995 he is a full Professor of automatics and informatics. His main research interests are the adaptation of new stochastic global optimization heuristics to various situations (multi objective mixed discrete-continuous variables, continuous variables, dynamic, etc.) and their application to various engineering fields. He is also interested in the fitting of process models to experimental data and thelearning of fuzzy rule bases and neural networks. P.Siarry is a senior member IEEE, an appointed member of the Technical Committee on Soft Computing of the IEEE systems, Man and Cybernetics (SMC) Society and an appointed member of the Technical Committee on Optimal Control (TC 2.4) of IFAC.

作者簡介(中文翻譯)

Jayaraman Valadi 博士是印度浦那 FLAME 大學的計算機科學特聘教授。他在浦那大學獲得化學博士學位。他的研究涵蓋多個領域，專注於化學和生物化學工程中的建模與模擬，以及過程建模、控制和優化。在過去的十年中，他致力於探索機器學習和人工智慧在各個領域的應用。他在多個知名國際期刊上發表了數十篇論文。自1976年開始他的職業生涯，Valadi 博士曾在印度工業與科學研究委員會 (CSIR) 工作了33年，並於2009年退休，擔任副主任。之後，他在浦那的高級計算發展中心擔任 CSIR 名譽科學家，直到2013年1月，隨後在印度大諾伊達的希夫納達大學擔任訪問教員，直到2023年5月。

Krishna Pratap Singh 博士是印度信息技術學院阿拉哈巴德 (IIITA) 信息技術系的副教授，並且負責機器學習與優化 (MLO) 實驗室。Singh 博士於2009年在印度理工學院魯爾基獲得優化博士學位，擁有超過15年的研究和學術經驗。他是日本櫻花科學俱樂部的成員，IEEE 高級會員及 ACM 會員。目前，他的研究小組正在研究低資源數據的遷移學習，並致力於在聯邦學習環境中開發模型。

Muneendra Ojha 博士是印度信息技術學院阿拉哈巴德 (IIITA) 信息技術系的助理教授，並領導人工智慧與多智能體系統 (AIMS) 實驗室。Ojha 博士在 IIITA 獲得博士學位，並在美國密蘇里大學哥倫比亞校區獲得碩士學位。Ojha 博士擁有超過19年的學術和行業經驗。他的研究興趣包括多目標優化、進化算法、語義網、自然語言處理、深度強化學習和多智能體系統。

Patrick Siarry 博士於1986年在巴黎第六大學獲得博士學位，並於1994年在巴黎第十一大學獲得科學博士學位（Habilitation）。他最初參與法國電力公司 (EDF) 核電廠的類比和數位模型開發。自1995年以來，他一直擔任自動化和資訊學的全職教授。他的主要研究興趣是將新的隨機全局優化啟發式方法適應於各種情況（多目標混合離散-連續變量、連續變量、動態等）及其在各工程領域的應用。他還對將過程模型擬合到實驗數據以及學習模糊規則庫和神經網絡感興趣。Siarry 博士是 IEEE 高級會員，IEEE 系統、人類與控制論 (SMC) 學會的軟計算技術委員會的委任成員，以及 IFAC 最優控制技術委員會 (TC 2.4) 的委任成員。

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