UID:
almafu_9961677519102883
Format:
1 online resource (106 pages)
Edition:
1st ed.
ISBN:
9783031643736
,
3031643739
Series Statement:
Synthesis Lectures on Learning, Networks, and Algorithms Series
Content:
This book, part of the 'Synthesis Lectures on Learning, Networks, and Algorithms' series, explores the role of artificial intelligence and computational methods in addressing epidemics. Authored by a team of computer scientists and bioinformaticians, it focuses on the application of evolutionary algorithms, genetic programming, and agent-based modeling to develop intelligent systems for vaccine deployment and epidemic management. The book was inspired by the COVID-19 pandemic, where the authors aimed to create effective strategies to minimize virus spread with limited vaccine supplies using combinatorial graph theory and contact network models. While traditional methods were ultimately used during the pandemic, the authors hope their tools will aid future epidemic planning and other complex networked systems. The book is intended for researchers and practitioners in computational sciences, public health, and related fields.
Note:
Preface -- Acknowledgments -- Contents -- 1 Introduction -- [DELETE] -- 1.1 Overview of Concepts -- 1.2 Organization of the Book -- 2 Evolutionary Computation -- [DELETE] -- 2.1 Evolutionary Algorithms -- 2.1.1 Representation -- 2.1.2 Fitness -- 2.1.3 Selection -- 2.1.4 Genetic Operators -- 2.2 Putting it Together -- 2.3 Genetic Programming -- 2.4 Representation and Language -- 2.5 Selection and Genetic Operators -- 2.6 Genetic Programming for Vaccine Distribution -- 2.7 The Takeaway -- 3 Graph Compression -- [DELETE] -- 3.1 Concepts of Graph Compression -- 3.1.1 Lossy Versus Lossless Compression -- 3.2 Unweighted Graph Compression -- 3.2.1 Representation -- 3.2.2 Initial Population -- 3.2.3 Selection -- 3.2.4 Crossover -- 3.2.5 Mutation
Additional Edition:
ISBN 9783031643729
Additional Edition:
ISBN 3031643720
Language:
English
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