| 000 | 03203nam a22005055i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-13425-8 | ||
| 003 | DE-He213 | ||
| 005 | 20140220084538.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100710s2010 gw | s |||| 0|eng d | ||
| 020 |
_a9783642134258 _9978-3-642-13425-8 |
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| 024 | 7 |
_a10.1007/978-3-642-13425-8 _2doi |
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| 050 | 4 | _aTA329-348 | |
| 050 | 4 | _aTA640-643 | |
| 072 | 7 |
_aTBJ _2bicssc |
|
| 072 | 7 |
_aMAT003000 _2bisacsh |
|
| 082 | 0 | 4 |
_a519 _223 |
| 100 | 1 |
_aSarker, Ruhul Amin. _eeditor. |
|
| 245 | 1 | 0 |
_aAgent-Based Evolutionary Search _h[electronic resource] / _cedited by Ruhul Amin Sarker, Tapabrata Ray. |
| 264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010. |
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| 300 |
_a291p. 48 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v5 |
|
| 505 | 0 | _aAgent Based Evolutionary Approach: An Introduction -- Multi-Agent Evolutionary Model for Global Numerical Optimization -- An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints -- Multiagent-Based Approach for Risk Analysis in Mission Capability Planning -- Agent Based Evolutionary Dynamic Optimization -- Divide and Conquer in Coevolution: A Difficult Balancing Act -- Complex Emergent Behaviour from Evolutionary Spatial Animat Agents -- An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller -- An Attempt to Stochastic Modeling of Memetic Systems -- Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm -- PSO (Particle Swarm Optimization): One Method, Many Possible Applications -- VISPLORE: Exploring Particle Swarms by Visual Inspection. | |
| 520 | _aThe performance of Evolutionary Algorithms can be enhanced by integrating the concept of agents. Agents and Multi-agents can bring many interesting features which are beyond the scope of traditional evolutionary process and learning. This book presents the state-of-the art in the theory and practice of Agent Based Evolutionary Search and aims to increase the awareness on this effective technology. This includes novel frameworks, a convergence and complexity analysis, as well as real-world applications of Agent Based Evolutionary Search, a design of multi-agent architectures and a design of agent communication and learning Strategy. | ||
| 650 | 0 | _aEngineering. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aMathematics. | |
| 650 | 0 | _aEngineering mathematics. | |
| 650 | 1 | 4 | _aEngineering. |
| 650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
| 650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
| 650 | 2 | 4 | _aApplications of Mathematics. |
| 700 | 1 |
_aRay, Tapabrata. _eeditor. |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783642134241 |
| 830 | 0 |
_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v5 |
|
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-13425-8 |
| 912 | _aZDB-2-ENG | ||
| 999 |
_c112255 _d112255 |
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