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| 001 | 330774 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230102114614.0 | ||
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| 007 | cr nn nnnaamaa | ||
| 008 | 210104s2021 gw | s |||| 0|eng d | ||
| 020 | _a9783030637736 | ||
| 024 | 7 |
_a10.1007/978-3-030-63773-6 _2doi |
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| 040 |
_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC |
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| 050 | 4 |
_aQA76.9.A43 _b2021 EB |
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| 100 | 1 |
_aSchütze, Oliver _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678146 |
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| 245 | 1 | 0 |
_aArchiving Strategies for Evolutionary Multi-objective Optimization Algorithms _cby Oliver Schütze, Carlos Hernández. |
| 250 | _aFirst edition 2021 | ||
| 264 | 1 |
_aCham _bSpringer International Pulishing _c2021 |
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| 300 |
_a1 recurso en línea (XIII, 234 páginas) _b130 ilustraciones, 44 ilustraciones a color |
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| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_atext file _bPDF _2 |
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| 490 | 0 |
_aStudies in Computational Intelligence _x1860-949X _v938 |
|
| 490 | 0 | _aIntelligent Technologies and Robotics (SpringerNature-42732) | |
| 490 | 0 | _aIntelligent Technologies and Robotics (R0) (SpringerNature-43728) | |
| 505 | 0 | _aIntroduction -- Multi-objective Optimization -- The Framework -- Computing the Entire Pareto Front -- Computing Gap Free Pareto Fronts -- Using Archivers within MOEAs -- Test Problems. | |
| 520 | 3 | _aThis book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization. | |
| 988 | _aSpringer_Robotics_2021 | ||
| 650 | 7 |
_aEstructuras de datos (Informática) _2embne _9151535 |
|
| 650 | 7 |
_aAlgoritmos computacionales _2embne _9151819 |
|
| 700 | 1 |
_aHernández, Carlos _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 776 | 0 | 8 |
_iPrinted edition: _z9783030637729 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030637743 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030637750 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-63773-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE _n0 |
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| 998 |
_b04/2021 _dz _eo _zSI |
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