Handbook of Heuristics

著者

• Martí, Rafael
• Pardalos, P. M. (Panos M.)
• Resende, Mauricio G. C.

書誌事項

Handbook of Heuristics

Rafael Martí, Panos M. Pardalos, Mauricio G.C. Resende, editors

（Springer reference）

Springer, c2018

• : [set]
• v. 1
• v. 2

注記

Includes bibliographical references and index

内容説明・目次

内容説明

Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a single method. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as 'rules of thumb' but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.

目次

Adaptive and Multilevel Metaheuristics Biased Random-Key Genetic Progamming Data Mining in Stochastic Local Search Evolution Strategies Matheuristics Multi-start Methods Multiobjective Optimization Restart Strategies Constraint-Based Local Search Guided Local Search Theory of Local Search Variable Neighborhood Descent Ant Colony Optimization: A Component-Wise Overview Evolutionary Algorithms Genetic Algorithms GRASP Hyper-Heuristics Iterated Greedy Iterated Local Search Memetic Algorithms Particle Swarm Methods POPMUSIC Random-Key Genetic Algorithms Scatter Search Tabu Search Variable Neighborhood Search A History of Metaheuristics Parallel Meta-heuristic Search Theoretical Analysis of Stochastic Search Algorithms City Logistics Cutting and Packing Diversity and Equity Models Evolutionary Algorithms for the Inverse Protein Folding Problem Linear Layout Problems Maritime Container Terminal Problems Metaheuristics for Medical Image Registration Metaheuristics for Natural Gas Pipeline Networks Network Optimization Optimization Problems, Models, and Heuristics in Wireless Sensor Networks Particle Swarm Optimization for the Vehicle Routing Problem: A Survey and a Comparative Analysis Scheduling Heuristics Selected String Problems Supply Chain Management The Maximum Clique and Vertex Coloring The multi-plant lot sizing problem with multiple periods and items Trees and Forests World's Best Universities and Personalized Rankings

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詳細情報

• NII書誌ID(NCID): BB26999231
• ISBN:
  • 9783319071237
• 出版国コード: sz
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Cham
• ページ数/冊数: 2 v.
• 大きさ: 25 cm
• 親書誌ID:
  • BA81675324