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001			94892
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006			m o d
007			cr un|---aucuu
008			161119s2016 sz o 101 0 eng d
020			_a3319490494 _q(electronic bk.)
020			_a9783319490496 _q(electronic bk.)
020			_z3319490486
020			_z9783319490489
035			_a(OCoLC)963785063 _z(OCoLC)962751040 _z(OCoLC)962793577 _z(OCoLC)963726448
040			_aEBLCP _cEBLCP _dOCLCO _dN$T _dIDEBK _dGW5XE _dOCLCQ _dOCLCF _dN$T _dUAB _dIOG _dESU _dJBG _dIAD _dICW _dICN _dILO _dOTZ _dYDX _dAZU _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQ342 _b.I584 2016 EB
111	2		_aAsia Pacific Symposium on Intelligent and Evolutionary Systems _n(20th : _d2016 : _cCanberra, A.C.T.)
245	1	0	_aIntelligent and evolutionary systems : _bthe 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings _cGeorge Leu, Hemant Kumar Singh, Saber Elsayed, editors.
246	3		_aIES 2016
264		1	_aCham _bSpringer _c2016.
300			_a1 recurso en línea (507 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aProceedings in Adaptation, Learning and Optimization _vv. 8
500			_aGenetic Programming with Embedded Feature Construction for High-Dimensional Symbolic Regression.
500			_aIncludes author index.
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aPreface; Organizing Committee; Conference Chair; Proceedings Chairs; Special Session Chairs; Registration Chair; Local Arrangement Chair; Publicity Chair; Sponsorship Chair; Program Committee; Contents; An Evolutionary Optimization Approach for Path Planning of Arrival Aircraft for Optimal Sequencing; 1 Introduction; 2 Problem Formulation; 2.1 Optimization Model for Arrival Sequencing Using Traditional GA; 2.2 Optimization Model for Arrival Sequencing Using Path Planning (TATA); 3 Methodology; 3.1 A Method for Generating the Optimal Arrival Sequence Using GA.
505	8		_a3 Impact of ALife Simulation of Darwinian and Lamarckian Evolutionary TheoriesAbstract; 1 Introduction; 2 Background and Problem Identification; 2.1 Engineering; 2.2 Computational Biology; 2.3 Philosophy and Ethics; 2.4 Research Objectives; 3 Research; 3.1 Fundamentals; 3.2 Proposal (Model); 3.3 Argument; 4 Conclusions; References; 4 A Local Search Algorithm for Saving Energy Cost in Duty-Cycle Wireless Sensor Network; Abstract; 1 Introduction; 2 Related Works; 3 Problem Formulation; 3.1 Duty-Cycle Wireless Sensor Networks Model; 3.2 Multicast on Duty-Cycle Wireless Sensor Network.
505	8		_a3.2 TATA for Optimal Arrival Sequence4 Experimental Design; 5 Result Analysis and Discussion; 6 Conclusion; References; 2 A Game-Theoretic Approach to the Analysis of Traffic Assignment; Abstract; 1 Introduction; 2 Stackelberg Game Based Modelling; 3 Gradient Projection Based Traffic Assignment; 4 Solution Methodology for the Stackelberg Game Based Traffic Assignment; 5 Numerical Experiment; 5.1 Convergence and Computation Performance of the Gradient Projection Method; 5.2 Travel Performance Comparison with SO and UE; 6 Conclusion; References.
505	8		_a3.4 Update the Velocity of the Collision Zone Particles4 Results and Discussions; 4.1 Path Planning by Obstacle Avoidance; 4.2 Path Planning with Multi-agent Systems; 5 Conclusions; References; 6 Resource Constrained Multi-project Scheduling: A Priority Rule Based Evolutionary Local Search Approach; Abstract; 1 Introduction; 2 Problem Description; 2.1 Nomenclature; 3 Algorithms; 3.1 ELSH-VN; 3.2 Priority Rules; 4 Experimental Results and Analysis; 4.1 Generation of Test Projects; 4.2 Experimental Results for Priority Rule Based ELSH-VN; 4.3 Statistical Comparison; 5 Conclusions; References.
505	8		_a4 Proposed Algorithm4.1 Searching for the Best Transport Schedule of Multicast Tree; 4.2 Finding the Base Solution; 4.3 Solution Initialization; 4.4 Complexity of Proposed Algorithm; 5 Experimental Results; 5.1 Problem Instances; 5.2 System Configuration; 5.3 Computational Results; 6 Conclusion; Acknowledgments; References; 5 Obstacle Avoidance for Multi-agent Path Planning Based on Vectorized Particle Swarm Optimization; Abstract; 1 Introduction; 2 Problem Description; 3 Particle Swarm Optimization; 3.1 Path Planning with SRVPSO; 3.2 Collision Avoidance Strategy; 3.3 Update the Fitness Value.
520	3		_aOver the last two decades the field of Intelligent Systems delivered to human kind significant achievements, while also facing major transformations. 20 years ago, automation and knowledge-based AI were still the dominant paradigms fueling the efforts of both researchers and practitioners. Later, 10 years ago, statistical machine intelligence was on the rise, heavily supported by the digital computing, and led to the unprecedented advances in and dependence on digital technology. However, the resultant intelligent systems remained designer-based endeavors and thus, were limited in their true learning and development abilities. Today, the challenge is to have in place intelligent systems that can develop themselves on behalf of their creators, and gain abilities with no or limited supervision in the tasks they are meant to perform. Cognitive development systems, and the supporting cognitive computing are on the rise today, promising yet other significant achievements for the future of human kind. This book captures this unprecedented evolution of the field of intelligent systems, presenting a compilation of studies that covers all research directions in the field over the last two decades, offering to the reader a broad view over the field, while providing a solid foundation from which outstanding new ideas may emerge.
650		7	_aInteligencia artificial _2embne _0(OCoLC)fst00817247 _0 _9413115
700	1		_aElsayed, Saber, _eeditor literario
700	1		_aLeu, George, _eeditor literario
700	1		_aSingh, Hemant Kumar, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-49049-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017A
998			_b02/2018 _dz _e- _zSI
999			_c94892 _d94892 _x1