Author Description

This Book Delves Into Evolutionary Large-scale Optimization, Integrating Evolutionary Computation With Machine Learning And Data Science. It Provides A Comprehensive Overview Of Evolutionary Algorithms And Their Application In Solving Complex Optimization Problems Across Various Domains, Including Community Detection, Logistics Scheduling, Power Systems, And Radiotherapy Planning. The Authors Discuss The Challenges Posed By Np-hard Problems And The Strategies For Overcoming Them Using Evolutionary Techniques. The Book Is Designed For Researchers And Practitioners In Artificial Intelligence, Offering Insights Into Emerging Applications And Performance Evaluation Of Evolutionary Algorithms. It Includes Matlab Codes For Practical Implementation And Experimentation. Cover -- Title Page -- Copyright -- Contents -- About The Authors -- Foreword -- Preface -- Acronyms -- Symbols -- Chapter 1 Multi‐objective Evolutionary Algorithms And Evolutionary Large‐scale Optimization -- 1.1 Introduction -- 1.2 Multi‐objective Evolutionary Algorithms (moeas) -- 1.2.1 Moeas For Solving Mops -- 1.2.2 Moeas For Solving Maops -- 1.3 Evolutionary Large‐scale Optimization -- 1.3.1 Decision Variable Grouping -- 1.3.2 Cooperative Coevolution -- 1.4 Summary -- References -- Chapter 2 Evolutionary Large‐scale Multi‐objective Optimization -- 2.1 Introduction -- 2.2 Test Problems For Large‐scale Multi‐objective Optimization -- 2.2.1 Primary Concepts -- 2.2.2 Multi‐objective Optimization Test Problems -- 2.2.3 Large‐scale Multi‐objective Optimization Test Problems -- 2.3 Performance Indicators -- 2.3.1 Performance Indicators Assessing Only Convergence -- 2.3.2 Performance Indicators Assessing Only Diversity -- 2.3.3 Performance Indicators Assessing Both Convergence And Diversity -- 2.4 Test Problems For Sparse Large‐scale Multi‐objective Optimization -- 2.4.1 Synthetic Test Problems -- 2.4.2 Practical Test Problems -- 2.4.2.1 Feature Selection -- 2.4.2.2 Instance Selection -- 2.4.2.3 Neural Network