- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
This book proposes an introduction to metaheuristics, combining a theoretical understanding with the practical skill to use and develop these methods. Optimization is central to most domains of science, whether academic or industrial. The solution to many real life problems rely on our ability to find the maximum or minimum of some quantity of interest. However, many of these problems are referred to as "hard optimization" problems, meaning that they quickly become numerically intractable and cannot be solved by traditional optimization techniques. Metaheuristics are methods, inspired by physical processes, Darwinian evolution, animal behaviors, and other phenomena observed in Nature, which usually find optimal values of satisfactory quality within acceptable computing resources. As such, they are an essential tool for the optimization community.
This textbook is suitable for advanced undergraduates in computer science and engineering, as well as for students and researchers from other disciplines looking for a concise and clear introduction to metaheuristic methods for optimization.
Contents
Problems, Algorithms, and Computational Complexity.- Search Space.- Statistical Features and Metrics of Search Spaces.- Tabu Search.- Simulated Annealing.- The Ant Colony Method.- Particle Swarm Optimization.- Fireflies, Cuckoos, and Lévy Flights.- Evolutionary Algorithms: Foundations.- Evolutionary Algorithms: structured populations.- Real Function Optimization: Evolution Strategies and Differential Evolution.- Genetic Programming.- Performance and Limitations of Metaheuristics.- Phase Transitions in Combinatorial Optimization Problems.- Adiabatic Quantum Computing and Quantum Annealing.
