Evolutionary multi-task optimization foundations and methodologies
Part I Introduction 1 Introduction 1.1 Optimization 1.2 Evolutionary Optimization 1.3 Evolutionary Multi-task Optimization 1.4 Organization of The Book 2 Preliminaries 2.1 Evolutionary Algorithms 2.2 Evolutionary Multi-task Optimization 2.3 Optimization as a Cloud-based Service 2.4 Eva...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Singapore
Springer
2023
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| Schriftenreihe: | Machine learning: foundations, methodologies, and applications
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| Online Zugang: | Cover |
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| Zusammenfassung: | Part I Introduction 1 Introduction 1.1 Optimization 1.2 Evolutionary Optimization 1.3 Evolutionary Multi-task Optimization 1.4 Organization of The Book 2 Preliminaries 2.1 Evolutionary Algorithms 2.2 Evolutionary Multi-task Optimization 2.3 Optimization as a Cloud-based Service 2.4 Evaluation of Multi-Task Optimization Part II Evolutionary Multi-task Optimization for Solving Continuous Optimization Problems 3 Multi-factorial Evolutionary Algorithm 3.1 Algorithm Design and Details 3.2 Empirical Study 3.3 Summary 4 Multi-factorial Evolutionary Algorithm with Adaptive Knowledge Transfer 4.1 Algorithm Design and Details 4.2 Empirical Study 4.3 Summary 5 Explicit Evolutionary Multi-task Optimization Algorithm 5.1 Algorithm Design and Details 5.2 Empirical Study 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 6.1 Generalized Vehicle Routing Problem 6.2 Algorithm Design and Details 6.3 Empirical Study 6.4 Summary 7 Explicit Evolutionary Multi-task optimization for Capacitated Vehicle Routing Problem 7.1 Capacitated Vehicle Routing Problem 7.2 Algorithm Design and Details 7.3 Empirical Study 7.4 Summary Part IV Evolutionary Multi-task Optimization for Solving Large-Scale Optimization Problems 8 Multi-Space Evolutionary Search for Large Scale Single-Objective Optimization 8.1 Challenges 8.2 Algorithm Design and Details 8.3 Empirical Study 8.4 Summary 9 Multi-Space Evolutionary Search for Large Scale Multi-Objective Optimization 9.1 Challenges 9.2 Algorithm Design and Details 9.3 Empirical Study 9.4 Summary A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness |
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| Beschreibung: | Literaturverzeichnis: Seite 207-219 |
| Beschreibung: | x, 219 Seiten Illustrationen, Diagramme |
| ISBN: | 9789811956492 978-981-19-5649-2 |