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|a 9781849961295
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|a Yu, Xinjie
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|a Introduction to Evolutionary Algorithms
|h Elektronische Ressource
|c by Xinjie Yu, Mitsuo Gen
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| 250 |
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|a 1st ed. 2010
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| 260 |
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|a London
|b Springer London
|c 2010, 2010
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| 300 |
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|a XVI, 422 p. 168 illus
|b online resource
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| 505 |
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|a Evolutionary Algorithms -- Simple Evolutionary Algorithms -- Advanced Evolutionary Algorithms -- Dealing with Complicated Problems -- Constrained Optimization -- Multimodal Optimization -- Multiobjective Optimization -- Combinatorial Optimization -- Brief Introduction to Other Evolutionary Algorithms -- Swarm Intelligence -- Artificial Immune Systems -- Genetic Programming
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| 653 |
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|a Artificial intelligence / Data processing
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| 653 |
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|a Control, Robotics, Automation
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| 653 |
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|a Applied Dynamical Systems
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| 653 |
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|a Computer simulation
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computer Modelling
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| 653 |
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|a Nonlinear theories
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| 653 |
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|a Control engineering
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Robotics
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| 653 |
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|a Automation
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| 653 |
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|a Data Science
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| 653 |
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|a Dynamics
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| 700 |
1 |
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|a Gen, Mitsuo
|e [author]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Decision Engineering
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| 028 |
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|a 10.1007/978-1-84996-129-5
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| 856 |
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|u https://doi.org/10.1007/978-1-84996-129-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline
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