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020 _a9783319440033
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024 7 _a10.1007/978-3-319-44003-3
_2doi
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072 7 _aTEC009000
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082 0 4 _a006.3
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245 1 0 _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.
300 _aXVI, 444 p. 198 illus., 107 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v663
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
650 0 _aArtificial intelligence.
_93407
650 0 _aMathematical optimization.
_94112
650 0 _aImage processing—Digital techniques.
_931565
650 0 _aComputer vision.
_952687
650 0 _aQuantitative research.
_94633
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.
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_952690
700 1 _aTrujillo, Leonardo.
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_952691
700 1 _aLegrand, Pierrick.
_eeditor.
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_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
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