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020 _a9783319640631
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024 7 _a10.1007/978-3-319-64063-1
_2doi
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072 7 _aTEC009000
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072 7 _aUYQ
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082 0 4 _a006.3
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245 1 0 _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 /
_cedited by Yazmin Maldonado, Leonardo Trujillo, Oliver Schütze, Annalisa Riccardi, Massimiliano Vasile.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 282 p. 146 illus., 124 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v731
505 0 _aPart I: Smart Cities -- Defensive Driving Strategy and Control for Autonomous Ground Vehicle in Mixed Traffic -- Augmenting the LSA Technique to Evaluate Ubicomp Environments -- Mixed Integer Programming Formulation for the Energy-Efficient Train Timetables Problem -- Distributing Computing in the Internet of Things: Cloud, Fog and Edge Computing Overview -- Part II: Search, Optimization and Hybrid Algorithms -- Integer Programming Models and Heuristics for Non-Crossing Euclidean 3-Matchings -- A Multi-Objective Robust Ellipse Fitting Algorithm -- Gradient-Based Multiobjective Optimization with Uncertainties -- A New Local Search Heuristic for the Multidimensional Assignment Problem -- Part III: Electronics and Embedded Systems --  A Multi-Objective and Multidisciplinary Optimisation Algorithm for Microelectromechanical Systems -- Coefficients Estimation of MPM through LSE, ORLS and SLS for RF-PA Modeling and DPD -- Optimal Sizing of Amplifiers by Evolutionary Algorithms with Integer Encoding and gm/ID Design Method -- Index.  .
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 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aImage processing—Digital techniques.
_931565
650 0 _aComputer vision.
_962368
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
700 1 _aMaldonado, Yazmin.
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700 1 _aTrujillo, Leonardo.
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700 1 _aSchütze, Oliver.
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700 1 _aRiccardi, Annalisa.
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_962372
700 1 _aVasile, Massimiliano.
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_962373
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783319877129
830 0 _aStudies in Computational Intelligence,
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856 4 0 _uhttps://doi.org/10.1007/978-3-319-64063-1
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