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Covering hybrid metaheuristics, parallel and distributed metaheuristics, and single-solution metaheursistics, Metaheuristics on Graphics Processing Units deftly explains how to design effective metaheuristics on GPUs for optimization and machine learning problems. Real-world examples and case studies, along with problems and solutions, demonstrate how metaheuristics are applied to such fields as telecommunication, logistics, and transportation. The veteran authors provide a complete background of metaheuristics and GPU that enables undergraduate and postgraduate students as well as engineers to design and deploy powerful algorithms to solve complex optimization problems in a diverse range of applications.