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024 7 _a10.1007/978-981-13-9263-4
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA76.9 .N37
_b2020 EB
245 0 0 _aApplied nature-inspired computing :
_balgorithms and case studies
_cedited by Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya
250 _aFirst edition
264 1 _aSingapore
_bSpringer Singapore :
_bImprint: Springer
_c2020
300 _a1 recurso en línea (XII, 275 páginas)
_b134 ilustraciones, 89 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aSpringer Tracts in Nature-Inspired Computing
_x2524-552X
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aChapter 1. Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation -- Chapter 2. Detection of Breast Cancer using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly algorithm and Optimum Path Forest Classification -- Chapter 3. Recommending Healthy Personalized Daily Menus - A Cuckoo Search based Hyper-Heuristic Approach -- Chapter 4. A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network -- Chapter 5. An Application of Binary Grey Wolf Optimizer (BGWO) variants for Unit Commitment Problem -- Chapter 6. Sensorineural hearing loss identification via discrete wavelet packet entropy and cat swarm optimization -- Chapter 7. Chaotic Variants of Grasshopper Optimisation Algorithm and their application to Protein Structure Prediction -- Chapter 8. Examination of Retinal Anatomical Structures - A Study with Spider Monkey Optimization Algorithm -- Chapter 9. Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power System: A Comparative Study -- Chapter 10. Parallel-series System Optimization by Weighting Sum Methods and Nature-inspired Computing -- Chapter 11. Development of Artificial Neural Networks trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends.
520 3 _aThis book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.
650 7 _2embne
_aBioinformática
_9160489
650 7 _2embne
_aBiología
_xModelos matemáticos
_9138150
650 7 _2embne
_aInteligencia artificial
_9413115
700 1 _aDey, Nilanjan,
_d1984-
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_998032
700 1 _aAshour, Amira,
_d1975-
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9670755
700 1 _aBhattacharyya, Siddhartha,
_d1975-
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9100498
776 0 8 _iPrinted edition:
_z9789811392627
776 0 8 _iPrinted edition:
_z9789811392641
776 0 8 _iPrinted edition:
_z9789811392658
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-9263-4
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _aSI
_cm
_dz
_feng
_ggw
_h0
_b12/2019
_eel
_zSI