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001 | 978-3-319-64063-1 | ||
003 | DE-He213 | ||
005 | 20211206131857.0 | ||
008 | 170913s2018 gw | s |||| 0|eng d | ||
020 | _a9783319640631 | ||
040 | _cAIKTC-KRRC | ||
041 | _aENG | ||
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_aNEO 2016 _h[electronic resource] : _bResults of the Numerical and Evolutionary Optimization Workshop NEO 2016 and the NEO Cities 2016 Workshop held on September 20-24, 2016 in Tlalnepantla, Mexico / |
250 | _a1st ed. 2018. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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300 |
_aXIII, 282 p. 146 illus., 124 illus. in color. _bCard Paper |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v731 |
|
520 | _aThis volume comprises a selection of works presented at the Numerical and Evolutionary Optimization (NEO 2016) workshop held in September 2016 in Tlalnepantla, Mexico. The development of powerful search and optimization techniques is of great importance in today’s world and requires researchers and practitioners to tackle a growing number of challenging real-world problems. In particular, there are two well-established and widely known fields that are commonly applied in this area: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics. Both paradigms have their unique strengths and weaknesses, allowing them to solve some challenging problems while still failing in others. The goal of the NEO workshop series is to bring together experts from these and related fields to discuss, compare and merge their complementary perspectives in order to develop fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of the underlying paradigms. In doing so, NEO promotes the development of new techniques that are applicable to a broader class of problems. Moreover, NEO fosters the understanding and adequate treatment of real-world problems particularly in emerging fields that affect all of us, such as healthcare, smart cities, big data, among many others. The extended papers presented in the book contribute to achieving this goal. . | ||
650 | 0 |
_aComputer Engineering _94622 |
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653 | _aComputational Intelligence. | ||
653 | _aArtificial Intelligence. | ||
653 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. | ||
700 | 1 |
_aMaldonado, Yazmin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aTrujillo, Leonardo. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aSchütze, Oliver. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aRiccardi, Annalisa. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aVasile, Massimiliano. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319640624 |
776 | 0 | 8 |
_iPrinted edition: _z9783319640648 |
776 | 0 | 8 |
_iPrinted edition: _z9783319877129 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v731 |
|
856 | 4 | 0 |
_uhttps://doi.org/10.1007/978-3-319-64063-1 _zClick here to access eBook in Springer Nature platform. (Within Campus only.) |
942 |
_cEBOOKS _2ddc |