Intro
Preface
Contents
Part I Background
1 Introduction
1.1 Optimization
1.2 Evolutionary Optimization
1.3 Evolutionary Multi-Task Optimization
1.4 Organization of the Book
2 Overview and Application-Driven Motivations of Evolutionary Multitasking
2.1 An Overview of EMT Algorithms
2.2 EMT in Real-World Problems
2.2.1 Category 1: EMT in Data Science Pipelines
2.2.2 Category 2: EMT in Evolving Embodied Intelligence
2.2.3 Category 3: EMT in Unmanned Systems Planning
2.2.4 Category 4: EMT in Complex Design

2.2.5 Category 5: EMT in Manufacturing, Operations Research
2.2.6 Category 6: EMT in Software and Services Computing
Part II Evolutionary Multi-Task Optimization for Solving Continuous Optimization Problems
3 The Multi-Factorial Evolutionary Algorithm
3.1 Algorithm Design and Details
3.1.1 Multi-Factorial Optimization
3.1.2 Similarity and Difference Between Multi-factorial Optimization and Multi-Objective Optimization
3.1.3 The Multi-Factorial Evolutionary Algorithm
3.1.3.1 Population Initialization
3.1.3.2 Genetic Mechanisms
3.1.3.3 Selective Evaluation

3.1.3.4 Selection Operation
3.1.3.5 Summarizing the Salient Features of the MFEA
3.2 Empirical Study
3.2.1 Multitasking Across Functions with Intersecting Optima
3.2.2 Multitasking Across Functions with Separated Optima
3.2.3 Discussions
3.3 Summary
4 Multi-Factorial Evolutionary Algorithm with Adaptive Knowledge Transfer
4.1 Algorithm Design and Details
4.1.1 Representative Crossover Operators for Continuous Optimization
4.1.2 Knowledge Transfer via Different Crossover Operators in MFEA
4.1.3 MFEA with Adaptive Knowledge Transfer

4.1.3.1 Adaptive Assortative Mating and Adaptive Vertical Cultural Transmission
4.1.3.2 Adaptation of Transfer Crossover Indicators
4.2 Empirical Study
4.2.1 Experimental Setup
4.2.2 Performance Metric
4.2.3 Results and Discussions
4.2.3.1 Common Multi-Task Benchmarks
4.2.3.2 Complex Multi-Task Problems
4.2.4 Other Issues
4.3 Summary
5 Explicit Evolutionary Multi-Task Optimization Algorithm
5.1 Algorithm Design and Details
5.1.1 Denoising Autoencoder
5.1.2 The Explicit EMT Paradigm
5.1.2.1 Learning of Task Mapping

5.1.2.2 Explicit Genetic Transfer Across Tasks
5.2 Empirical Study
5.2.1 Single-Objective Multi-Task Optimization
5.2.1.1 Experiment Setup
5.2.1.2 Results and Discussions
5.2.2 Multi-Objective Multi-Task Optimization
5.2.2.1 Experiment Setup
5.2.2.2 Results and Discussions
5.3 Summary
Part III Evolutionary Multi-Task Optimization for Solving Combinatorial Optimization Problems
6 Evolutionary Multi-Task Optimization for Generalized Vehicle Routing Problem with Occasional Drivers