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03650nam a22005895i 4500 |
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978-1-84996-129-5 |
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DE-He213 |
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20151204174713.0 |
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cr nn 008mamaa |
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100609s2010 xxk| s |||| 0|eng d |
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|a 9781849961295
|9 978-1-84996-129-5
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|a 10.1007/978-1-84996-129-5
|2 doi
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|d GrThAP
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|a Q334-342
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|a TJ210.2-211.495
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|a COM004000
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|a 006.3
|2 23
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|a Yu, Xinjie.
|e author.
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|a Introduction to Evolutionary Algorithms
|h [electronic resource] /
|c by Xinjie Yu, Mitsuo Gen.
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| 264 |
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|a London :
|b Springer London,
|c 2010.
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| 300 |
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|a XVI, 422 p. 168 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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| 490 |
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|a Decision Engineering,
|x 1619-5736 ;
|v 0
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| 505 |
0 |
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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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| 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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| 650 |
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|a Computer science.
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| 650 |
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|a Data structures (Computer science).
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computer simulation.
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| 650 |
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|a Complexity, Computational.
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| 650 |
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|a Control engineering.
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| 650 |
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|a Robotics.
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|a Mechatronics.
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4 |
|a Computer Science.
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| 650 |
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|a Artificial Intelligence (incl. Robotics).
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| 650 |
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4 |
|a Data Structures.
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| 650 |
2 |
4 |
|a Complexity.
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| 650 |
2 |
4 |
|a Control, Robotics, Mechatronics.
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| 650 |
2 |
4 |
|a Simulation and Modeling.
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| 700 |
1 |
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|a Gen, Mitsuo.
|e author.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9781849961288
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| 830 |
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|a Decision Engineering,
|x 1619-5736 ;
|v 0
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-1-84996-129-5
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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