http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
the 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings
Asia Pacific Symposium on Intelligent and Evolutionary Systems 2016 : Canberra, A.C.T.)
Over 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.
Preface; 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.
3.2 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.
3 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.
4 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.
3.4 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.
George Leu, Hemant Kumar Singh, Saber Elsayed, editors.
Genetic Programming with Embedded Feature Construction for High-Dimensional Symbolic Regression.
Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
Intelligent and Evolutionary Systems : The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings
Cham : Springer International Publishing, ©2016
Proceedings in adaptation, learning and optimization ; v. 8