NEO 2016 Results of the Numerical and Evolutionary Optimization Workshop NEO 2016 and the NEO Cities 2016 Workshop held on September 20-24, 2016 in Tlalnepantla, Mexico /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Maldonado, Yazmin (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Trujillo, Leonardo (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Schütze, Oliver (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Riccardi, Annalisa (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Vasile, Massimiliano (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Studies in Computational Intelligence, 731
• Θέματα: Computational intelligence.; Artificial intelligence.; Optical data processing.; Computational Intelligence.; Artificial Intelligence.; Computer Imaging, Vision, Pattern Recognition and Graphics.

 This 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.   . 

 Part 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.  .