Swarm Intelligence and Evolutionary Computation: Theory, Advances and Applications in Machine Learning and Deep Learning

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.

本書的目的是介紹和分析群體和演化智能的理論進展以及新興的實際應用。本書共分為九章。第一章提供了有關計算優化技術的理論介紹，包括基於梯度的方法（如最速下降法、共軛梯度法、牛頓法和拟牛頓法）以及非梯度方法（如遺傳算法和群體智能算法）。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能理論和粒子群優化算法。此外，還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、帶突變的粒子群優化、混沌粒子群優化、多目標粒子群優化和基於量子力學的粒子群優化算法。第四章介紹了兩種重要的仿生算法：螞蟻優化算法（ACO）和人工蜂群算法（ABC）。第五章介紹和分析了布穀鳥搜索算法和蝙蝠群算法及其最新的變體。第六章討論了其他幾種元啟發式算法，如萤火虫算法（FA）、和谐搜索（HS）、猫群优化（CSO）及其改進的算法修改。第七章討論了最新的仿生群算法，如灰狼優化算法（GWO）、鯨魚優化算法（WOA）、蚱蜢優化算法（GOA）以及二進制和混沌版本等其他算法變體。第八章介紹了群體和演化算法在機器學習中的應用。通過使用遺傳算法、幾何粒子群優化、混沌和谐搜索、混沌布穀鳥搜索和演化算法，提供了有關神經網絡優化和特徵選擇的實際案例，以及使用群體優化支持向量機進行犯罪預測。第九章討論了群體智能在深度長短期記憶（LSTM）網絡和深度卷積神經網絡（CNN）上的應用，包括LSTM超參數調整和從胸部X射線圖像中診斷Covid19。本書的目的是介紹和討論幾種最先進的群體智能和演化算法，以及它們的變體，並提供幾個機器學習和深度學習的實際應用案例。

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.

Georgios N. Kouziokas是希臘塞薩利大學的講師。他擁有塞薩利大學人工智慧決策系統領域的博士學位。他擁有四個碩士學位，分別是：計算機科學、應用數學、教育、地理信息系統和環境空間分析，以及一個計算機科學的學士學位。

他是兩個國際期刊的編輯，並擔任多個國際期刊的編輯委員會成員和副編輯。他曾為60多個國際期刊審稿。他因其博士學位的成就於2018年獲得美國伊利諾伊大學頒發的新興學者獎。此外，他還因其在2018年和2019年被Web of Science的Publons組織評為頂級同行評審專家而獲獎。

他在Elsevier和Springer等主要出版商的同行評審國際科學期刊、專書章節和會議論文中發表了45多篇論文。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人工智慧、計算智能和優化、群體智能、機器學習、深度學習、神經模糊邏輯、應用數學、信息系統、教育信息學、環境信息學、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、圖像處理/遙感-地理信息系統中的人工智慧、機器人技術、量子人工智慧和網絡安全。