Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep Learning
暫譯: 群體智慧與演化計算：機器學習與深度學習的理論、進展與應用

Kouziokas, Georgios

Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep Learning
  • 出版商: CRC
  • 出版日期: 2023-03-07
  • 售價: $5,500
  • 貴賓價: 9.5 折 $5,225
  • 語言: 英文
  • 頁數: 204
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032162503
  • ISBN-13: 9781032162508
  • 相關分類: ARM、Machine Learning、DeepLearning

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商品描述

The aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises nine chapters. Chapter 1 provides a theoretical introduction of the computational optimization techniques regarding the gradient-based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. Chapter 2, discusses evolutionary computation techniques and genetic algorithm. Swarm intelligence theory and particle swarm optimization algorithm are reviewed in Chapter 3. Also, several variations of particle swarm optimization algorithm are analysed and explained such as Geometric PSO, PSO with mutation, Chaotic PSO with mutation, multi-objective PSO and Quantum mechanics - based PSO algorithm. Chapter 4 deals with two essential colony bio-inspired algorithms: Ant colony optimization (ACO) and Artificial bee colony (ABC). Chapter 5, presents and analyses Cuckoo search and Bat swarm algorithms and their latest variations. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO) and their improved algorithm modifications. The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and other algorithm variations such as binary and chaotic versions. Chapter 8 presents machine learning applications of swarm and evolutionary algorithms. Illustrative real-world examples are presented with real datasets regarding neural network optimization and feature selection, using: genetic algorithm, Geometric PSO, Chaotic Harmony Search, Chaotic Cuckoo Search, and Evolutionary Algorithm and also crime forecasting using swarm optimized SVM. In chapter 9, applications of swarm intelligence on deep long short-term memory (LSTM) networks and Deep Convolutional Neural Networks (CNNs) are discussed, including LSTM hyperparameter tuning and Covid19 diagnosis from chest X-Ray images. The aim of the book is to present and discuss several state-of-theart swarm intelligence and evolutionary algorithms together with their variances and also several illustrative applications on machine learning and deep learning.

商品描述(中文翻譯)

本書的目的是介紹和分析群體智慧及演化智慧的理論進展和新興實際應用。全書共分為九章。第一章提供了計算優化技術的理論介紹，涵蓋基於梯度的方法，如最速下降法、共軛梯度法、牛頓法和準牛頓法，以及非梯度方法，如遺傳算法和群體智慧算法。第二章討論了演化計算技術和遺傳算法。第三章回顧了群體智慧理論和粒子群優化算法，並分析和解釋了幾種粒子群優化算法的變體，如幾何粒子群優化（Geometric PSO）、帶突變的粒子群優化（PSO with mutation）、帶突變的混沌粒子群優化（Chaotic PSO with mutation）、多目標粒子群優化（multi-objective PSO）和基於量子力學的粒子群優化算法。第四章處理兩種基本的生物啟發算法：螞蟻群優化（Ant colony optimization, ACO）和人工蜜蜂群（Artificial bee colony, ABC）。第五章介紹和分析了杜鵑搜索（Cuckoo search）和蝙蝠群算法（Bat swarm algorithms）及其最新變體。第六章討論了幾種其他的元啟發式算法，如螢火蟲算法（Firefly algorithm, FA）、和諧搜索（Harmony search, HS）、貓群優化（Cat swarm optimization, CSO）及其改進的算法變體。第七章討論了最新的生物啟發群體算法，如灰狼優化算法（Grey Wolf Optimization, GWO）、鯨魚優化算法（Whale Optimization Algorithm, WOA）、蝗蟲優化算法（Grasshopper Optimization Algorithm, GOA）及其他算法變體，如二進制和混沌版本。第八章介紹了群體和演化算法在機器學習中的應用，提供了使用實際數據集的示例，涉及神經網絡優化和特徵選擇，使用的算法包括遺傳算法、幾何粒子群優化、混沌和諧搜索、混沌杜鵑搜索和演化算法，以及使用群體優化的支持向量機（SVM）進行犯罪預測。在第九章中，討論了群體智慧在深度長短期記憶（LSTM）網絡和深度卷積神經網絡（CNNs）上的應用，包括LSTM超參數調整和從胸部X光影像進行Covid19診斷。本書的目的是介紹和討論幾種最先進的群體智慧和演化算法及其變體，以及在機器學習和深度學習中的幾個示例應用。

作者簡介

Georgios N. Kouziokas is a Lecturer at the University of Thessaly, Greece. He holds a Ph.D. in artificial intelligence in decision systems from the University of Thessaly. He holds four Masters of Science (MSc) in: computer science, applied mathematics, education, geographic information systems and environmental spatial analysis and a BSc in computer science.

He serves as an editor in two international journals about the application of artificial intelligence, editorial board member and associate editor in several international journals. He has reviewed for more than 60 international journals. He was awarded with the Emerging Scholar Award 2018 by the University of Illinois, USA for his Ph.D. achievements. Also, he was awarded with the Top Peer Reviewer Award 2018, 2019 by Publons organization, part of Web of Science.

He has more than 45 publications in peer-reviewed international scientific journals, book chapters and conference proceedings from major publishers, like Elsevier and Springer. He has served as a member of the organizing committee, program chair in several international conferences. His major research areas include work related to Artificial Intelligence, Computational Intelligence and Optimization, Swarm Intelligence, Machine Learning, Deep Learning, Neuro-Fuzzy Logic, Applied Mathematics, Information Systems, Educational Informatics, Environmental Informatics, Data Analysis, AI in Education, AI in Public Management, AI in justice, AI in Image Processing/Remote Sensing - Geographic Information Systems, Robotics, Quantum Artificial Intelligence and Cyber-Security.

作者簡介(中文翻譯)

Georgios N. Kouziokas 是希臘塞薩利大學的講師。他擁有塞薩利大學的人工智慧決策系統博士學位。他擁有四個科學碩士學位（MSc），分別為：計算機科學、應用數學、教育、地理資訊系統及環境空間分析，並擁有計算機科學的學士學位（BSc）。

他擔任兩本關於人工智慧應用的國際期刊的編輯，並在多本國際期刊擔任編輯委員會成員及副編輯。他為超過60本國際期刊進行過審稿。他因其博士學位的成就而獲得美國伊利諾伊大學2018年新興學者獎。此外，他還獲得了Publons組織（Web of Science的一部分）頒發的2018年和2019年最佳同行評審獎。

他在同行評審的國際科學期刊、書籍章節和主要出版商（如Elsevier和Springer）的會議論文中發表了超過45篇出版物。他曾擔任多個國際會議的組織委員會成員和程序主席。他的主要研究領域包括與人工智慧、計算智能與優化、群體智能、機器學習、深度學習、神經模糊邏輯、應用數學、資訊系統、教育資訊學、環境資訊學、數據分析、教育中的人工智慧、公共管理中的人工智慧、司法中的人工智慧、影像處理/遙感中的人工智慧 - 地理資訊系統、機器人技術、量子人工智慧和網絡安全相關的工作。

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