000 | 04318nam a22006375i 4500 | ||
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007 | cr nn 008mamaa | ||
008 | 160823s2017 sz | s |||| 0|eng d | ||
020 |
_a9783319440033 _9978-3-319-44003-3 |
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_a10.1007/978-3-319-44003-3 _2doi |
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_aNEO 2015 _h[electronic resource] : _bResults of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico / _cedited by Oliver Schütze, Leonardo Trujillo, Pierrick Legrand, Yazmin Maldonado. |
250 | _a1st ed. 2017. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
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300 |
_aXVI, 444 p. 198 illus., 107 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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_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v663 |
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505 | 0 | _aPart I Genetic Programming -- Part II Combinatorial Optimization -- Part IV Machine Learning and Real World Applications. | |
520 | _aThis volume comprises a selection of works presented at the Numerical and Evolutionary Optimization (NEO) workshop held in September 2015 in Tijuana, Mexico. The development of powerful search and optimization techniques is of great importance in today’s world that 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 people from these and related fields to discuss, compare and merge their complimentary perspectives in order to develop fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of the underlying paradigms. Through this effort, we believe that the NEO can promote 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 us all such as health care, smart cities, big data, among many others. The extended papers the NEO 2015 that comprise this book make a contribution to this goal. | ||
650 | 0 |
_aComputational intelligence. _97716 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aMathematical optimization. _94112 |
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650 | 0 |
_aImage processing—Digital techniques. _931565 |
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650 | 0 |
_aComputer vision. _952687 |
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650 | 0 |
_aQuantitative research. _94633 |
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650 | 1 | 4 |
_aComputational Intelligence. _97716 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aOptimization. _952688 |
650 | 2 | 4 |
_aComputer Imaging, Vision, Pattern Recognition and Graphics. _931569 |
650 | 2 | 4 |
_aData Analysis and Big Data. _952689 |
700 | 1 |
_aSchütze, Oliver. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _952690 |
|
700 | 1 |
_aTrujillo, Leonardo. _eeditor. _0(orcid)0000-0003-1812-5736 _1https://orcid.org/0000-0003-1812-5736 _4edt _4http://id.loc.gov/vocabulary/relators/edt _952691 |
|
700 | 1 |
_aLegrand, Pierrick. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _952692 |
|
700 | 1 |
_aMaldonado, Yazmin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _952693 |
|
710 | 2 |
_aSpringerLink (Online service) _952694 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319440026 |
776 | 0 | 8 |
_iPrinted edition: _z9783319440040 |
776 | 0 | 8 |
_iPrinted edition: _z9783319829579 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v663 _952695 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-44003-3 |
912 | _aZDB-2-ENG | ||
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