TY - GEN digital ID - 131565087 TI - Search and Optimization by Metaheuristics : Techniques and Algorithms Inspired by Nature AU - Du, Ke-Lin AU - Swamy, M. N. S. PY - 2016 SN - 9783319411927 PB - Cham Springer International Publishing, Imprint: Birkhäuser DB - UniCat KW - Numerical methods of optimisation KW - Operational research. Game theory KW - Mathematics KW - Computer science KW - Artificial intelligence. Robotics. Simulation. Graphics KW - Computer. Automation KW - neuronale netwerken KW - fuzzy logic KW - cybernetica KW - vormgeving KW - automatisering KW - computers KW - informatica KW - mineralen (chemie) KW - simulaties KW - mijnbouw KW - wiskunde KW - algoritmen KW - informaticaonderzoek KW - KI (kunstmatige intelligentie) KW - AI (artificiële intelligentie) UR - https://www.unicat.be/uniCat?func=search&query=sysid:131565087 AB - This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics. Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods. ER -