Evolutionary Multi-Task Optimization Foundations and Methodologies /
Feng, Liang.
Evolutionary Multi-Task Optimization Foundations and Methodologies / [electronic resource] : by Liang Feng, Abhishek Gupta, Kay Chen Tan, Yew Soon Ong. - 1st ed. 2023. - X, 219 p. 1 illus. online resource. - Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 . - Machine Learning: Foundations, Methodologies, and Applications, .
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.
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. .
9789811956508
10.1007/978-981-19-5650-8 doi
Artificial intelligence.
Machine learning.
Mathematical optimization.
Computational intelligence.
Artificial Intelligence.
Machine Learning.
Optimization.
Computational Intelligence.
Q334-342 TA347.A78
006.3
Evolutionary Multi-Task Optimization Foundations and Methodologies / [electronic resource] : by Liang Feng, Abhishek Gupta, Kay Chen Tan, Yew Soon Ong. - 1st ed. 2023. - X, 219 p. 1 illus. online resource. - Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 . - Machine Learning: Foundations, Methodologies, and Applications, .
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.
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. .
9789811956508
10.1007/978-981-19-5650-8 doi
Artificial intelligence.
Machine learning.
Mathematical optimization.
Computational intelligence.
Artificial Intelligence.
Machine Learning.
Optimization.
Computational Intelligence.
Q334-342 TA347.A78
006.3