Handbook of Heuristics
Author(s)
Bibliographic Information
Handbook of Heuristics
(Springer reference)
Springer, c2018
- : [set]
- v. 1
- v. 2
Available at / 5 libraries
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Kyoto University of Advance Sience Library太秦南館
v. 1007.64||H29||110405007,
v. 2007.64||H29||210405008 -
The University of Electro-Communications Library研
v. 1548.964/H29/12018101579,
v. 2548.964/H29/22018101580 -
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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
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