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03852nam a22005295i 4500 |
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978-3-642-24094-2 |
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|a 9783642240942
|9 978-3-642-24094-2
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|a 10.1007/978-3-642-24094-2
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|a Nature Inspired Cooperative Strategies for Optimization (NICSO 2011)
|h [electronic resource] /
|c edited by David Alejandro Pelta, Natalio Krasnogor, Dan Dumitrescu, Camelia Chira, Rodica Lung.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2011.
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|a XXII, 350 p.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|b PDF
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 387
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|a From the content: Ant Colony Optimization for Automatic Design of Strategies in an Adversarial Model -- Resource Allocation and Dispensation Impact of Stochastic Diffusion Search on Differential Evolution Algorithm -- An Adaptive Multiagent Strategy for Solving Combinatorial Dynamic Optimization Problems -- Interactive Intonation Optimisation Using CMA-ES and DCT Parameterisation of the F0 Contour for Speech Synthesis -- Genotype-Fitness Correlation Analysis for Evolutionary Design of Self-Assembly Wang Tiles -- Model Order Reduction of Single Input Single Output Systems using Artificial Bee Colony Optimization Algorithm -- Lamps: A Test Problem for Cooperative Coevolution.
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|a Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation. The previous editions of NICSO were held in Granada, Spain (2006), Acireale, Italy (2007), Tenerife, Spain (2008), and again in Granada in 2010. NICSO evolved to be one of the most interesting and profiled workshops in nature inspired computing. NICSO 2011 has offered an inspiring environment for debating the state of the art ideas and techniques in nature inspired cooperative strategies and a comprehensive image on recent applications of these ideas and techniques. The topics covered by this volume include Swarm Intelligence (such as Ant and Bee Colony Optimization), Genetic Algorithms, Multiagent Systems, Coevolution and Cooperation strategies, Adversarial Models, Synergic Building Blocks, Complex Networks, Social Impact Models, Evolutionary Design, Self Organized Criticality, Evolving Systems, Cellular Automata, Hybrid Algorithms, and Membrane Computing (P-Systems).
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|a Engineering.
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|a Artificial intelligence.
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|a Statistics.
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|a Computational intelligence.
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|a Engineering.
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|a Computational Intelligence.
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|a Artificial Intelligence (incl. Robotics).
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|a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
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| 700 |
1 |
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|a Pelta, David Alejandro.
|e editor.
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| 700 |
1 |
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|a Krasnogor, Natalio.
|e editor.
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| 700 |
1 |
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|a Dumitrescu, Dan.
|e editor.
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| 700 |
1 |
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|a Chira, Camelia.
|e editor.
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| 700 |
1 |
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|a Lung, Rodica.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783642240935
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 387
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|u http://dx.doi.org/10.1007/978-3-642-24094-2
|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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