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008			170310s2017 sz a ob 000 0 eng d
020			_a3319509195
020			_a3319509209 _q(electronic bk.)
020			_a9783319509198
020			_a9783319509204 _q(electronic bk.)
020			_z9783319509198 _q(print)
040			_aN$T _cN$T _dIDEBK _dGW5XE _dN$T _dEBLCP _dOCLCF _dYDX _dUAB _dNJR _dIOG _dAZU _dUPM _dMERER _dESU _dOCLCQ _dJBG _dIAD _dICW _dICN _dVT2 _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA76.9.N37 _bN388 2017 EB
245	0	0	_aNature-inspired computing and optimization : _btheory and applications _cSrikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu, editors.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xxi, 494 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aModeling and optimization in science and technologies _x2196-7326 _vvolume 10
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aPreface; Contents; Contributors; Members of Review Board; The Nature of Nature: Why Nature-Inspired Algorithms Work; 1 Introduction: How Nature Works; 2 The Nature of Nature; 2.1 Fitness Landscape; 2.2 Graphs and Phase Changes; 3 Nature-Inspired Algorithms; 3.1 Genetic Algorithm; 3.2 Ant Colony Optimization; 3.3 Simulated Annealing; 3.4 Convergence; 4 Dual-Phase Evolution; 4.1 Theory; 4.2 GA; 4.3 Ant Colony Optimization; 4.4 Simulated Annealing; 5 Evolutionary Dynamics; 5.1 Markov Chain Models; 5.2 The Replicator Equation; 6 Generalized Local Search Machines; 6.1 The Model; 6.2 SA; 6.3 GA.
505	8		_a2.1 Bin Packing Problem2.2 Graph Colouring Problem; 2.3 Travelling Salesman Problem; 3 Previous Work on Small Travelling Salesman Problem Instances; 4 Algorithms; 4.1 2-Opt; 4.2 Lin -- Kernighan; 4.3 Genetic Algorithm Variations; 4.4 Representation; 5 Experimental Design; 5.1 Bin Packing Problem; 5.2 Graph Colouring Problem; 5.3 Travelling Salesman Problem; 6 Results and Discussion; 6.1 Bin Packing Problem; 6.2 Graph Colouring Problem; 6.3 Travelling Salesman Problem; 7 Conclusions; References.
505	8		_a2.1 Combinatorial Optimisation Problems2.2 Multi-objective Combinatorial Optimisation Problems; 2.3 Pareto Optimality; 2.4 Decision-Making; 2.5 Solving Combinatorial Optimisation Problems; 3 Multi-objective Ant Colony Optimisation; 3.1 Origins; 3.2 Multi-objective Ant Colony Optimisation; 4 Applications of MOACO Algorithms in WSNs; 5 Conclusion; References; Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence; 1 Introduction; 2 Artificial Sports Trainer; 3 Generating the Training Plans; 3.1 Preprocessing; 3.2 Optimization Process; 4 Experiments.
505	8		_a5 Conclusion with Future IdeasReferences; Limiting Distribution and Mixing Time for Genetic Algorithms; 1 Introduction; 2 Preliminaries; 2.1 Random Search and Markov Chains; 2.2 Boltzmann Distribution and Simulated Annealing; 3 Expected Hitting Time as a Means of Comparison; 3.1 ``No Free Lunch'' Considerations; 4 The Holland Genetic Algorithm; 5 A Simple Genetic Algorithm; 6 Shuffle-Bit GA; 6.1 Results; 6.2 Estimate of Expected Hitting Time; 7 Discussion and Future Work; References; Permutation Problems, Genetic Algorithms, and Dynamic Representations; 1 Introduction; 2 Problem Descriptions.
505	8		_a6.4 ACO6.5 Discussion; 7 Conclusion; References; Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments; 1 Introduction; 2 Improved Bat Algorithm; 3 IBA for Multimodal Problems; 3.1 Parameter Settings; 3.2 Test Functions; 3.3 Numerical Results; 4 Performance Comparison of IBA with Other Algorithms; 5 IBA Performance in AWGN; 5.1 Numerical Results; 6 Conclusions; References; Multi-objective Ant Colony Optimisation in Wireless Sensor Networks; 1 Introduction; 2 Multi-objective Combinatorial Optimisation Problems.
505	8		_aHybridization of the Flower Pollination Algorithm -- A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults.
520	3		_aThe book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.
650		7	_9145705 _aOptimización matemática _2fast _0(OCoLC)fst01012099 _0
700	1		_aNakamatsu, Kazumi, _eeditor literario _999181
700	1		_aPatnaik, Srikanta, _eeditor literario
700	1		_aYang, Xin-She, _eeditor literario _997941
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-50920-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95543 _d95543 _x1