Evolutionary Multi-Task Optimization
Foundations and Methodologies
Series: Machine Learning: Foundations, Methodologies, and Applications;
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Product details:
- Edition number 1st ed. 2023
- Publisher Springer
- Date of Publication 2 April 2025
- Number of Volumes 1 pieces, Book
- ISBN 9789811956522
- Binding Paperback
- No. of pages219 pages
- Size 235x155 mm
- Language English
- Illustrations 1 Illustrations, black & white 700
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
Chapter 1.Introduction.- Chapter 2. Overview and Application-driven Motivations of Evolutionary Multitasking.- Chapter 3.The Multi-factorial Evolutionary Algorithm.- Chapter 4. Multi-factorial Evolutionary Algorithm with Adaptive Knowledge Transfer.- Chapter 5.Explicit Evolutionary Multi-task Optimization Algorithm.- Chapter 6.Evolutionary Multi-task Optimization for Generalized Vehicle Routing Problem With Occasional Drivers.- Chapter 7. Explicit Evolutionary Multi-task Optimization for Capacitated Vehicle Routing Problem.- Chapter 8. Multi-Space Evolutionary Search for Large Scale Single-Objective Optimization.- Chapter 9.Multi-Space Evolutionary Search for Large-scale Multi-Objective Optimization.
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