CRAI - Universidad del Rosario Koha › Detalles de: Intelligent optimization principles, algorithms and applications

Intelligent optimization : principles, algorithms and applications

por Li, Changhe

Autores adicionales: Han, Shoufei | Zeng, Sanyou | Yang, Shengxiang

Declaración de edición:1a. Ed Publicado por : Springer ; Hubei Province : China University of Geosciences Press (Singapore ) Detalles físicos: xxiii, 361 Paginas ilustraciones a color ISBN:9789819732852; 9789819732869.

Tema(s): Computación evolutiva -- Programación (matemáticas) | Teoría de optimización -- Análisis de sistemas | Inteligencia artificial -- Algoritmos computacionales

Año : 2024

Existencias ( 1 )
Notas de título ( 1 )

Tipo de ítem	Ubicación actual	Colección	Signatura	Copia número	Estado	Fecha de vencimiento	Código de barras	Reserva de ítems
Libro	Claustro 4to piso	Libro	006.3 L693i (Navegar estantería)	Ej.1	Disponible		100173363

Total de reservas: 0

Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- Part I Introduction and Fundamentals -- 1 Introduction -- 1.1 Optimization and Machine Learning -- 1.2 Optimization Problems -- 1.2.1 Mathematical Formulation -- 1.2.2 Continuous Optimization Versus Discrete Optimization -- 1.3 Optimization Algorithms -- 1.3.1 Deterministic Algorithms and Probabilistic Algorithms -- 1.3.2 Intelligent Optimization Techniques -- References -- 2 Fundamentals -- 2.1 Fitness Landscapes -- 2.1.1 Solution Space -- 2.1.2 Objective Space -- 2.1.3 Neighbourhood -- 2.1.4 Global Optimum -- 2.1.5 Local Optimum
2.2 Properties of Fitness Landscape -- 2.2.1 Modality -- 2.2.2 Ruggedness -- 2.2.3 Deceptiveness -- 2.2.4 Neutrality -- 2.2.5 Separability -- 2.2.6 Scalability -- 2.2.7 Domino Convergence -- 2.2.8 Property Control, Analysis, and Visualization -- 2.3 Computational Complexity -- 2.3.1 Complexity Measures -- 2.3.1.1 Time Complexity -- 2.3.1.2 Space Complexity -- 2.3.1.3 Ways of Measures -- 2.3.1.4 Time Versus Space -- 2.3.2 P Versus NP Problem -- References -- 3 Canonical Optimization Algorithms -- 3.1 Numerical Optimization Algorithms -- 3.1.1 Line Search -- 3.1.2 Steepest Descent Method
3.1.3 Newton Method -- 3.1.4 Conjugate Gradient Method -- 3.2 State Space Search -- 3.2.1 State Space -- 3.2.1.1 The Shortest Path Problem -- 3.2.1.2 The Travelling Salesman Problem -- 3.2.2 Uninformed Search -- 3.2.2.1 Breadth-First Search -- 3.2.2.2 Depth-First Search -- 3.2.2.3 Depth-Limited Search -- 3.2.3 Informed Search -- 3.2.3.1 Greedy Search -- 3.2.3.2 A* Search -- 3.2.3.3 Monte-Carlo Tree Search -- 3.3 Single-Solution-Based Random Search -- 3.3.1 Hill Climbing -- 3.3.2 Simulated Annealing -- 3.3.3 Iterated Local Search -- 3.3.4 Variable Neighborhood Search -- References
Part II Evolutionary Computation Algorithms -- 4 Basics of Evolutionary Computation Algorithms -- 4.1 Introduction -- 4.1.1 Biological Evolution -- 4.1.2 Origin of Evolutionary Algorithms -- 4.1.3 Basic Evolutionary Processes -- 4.1.4 Developments -- 4.1.5 Related Resources -- 4.2 Solution Representation -- 4.2.1 Binary Representation -- 4.2.2 Integer Representation -- 4.2.3 Real-Valued Representation -- 4.2.4 Tree Representation -- 4.2.5 The Effect of Representation -- 4.3 Selection -- 4.3.1 Parents Selection -- 4.3.2 Survivor Selection -- 4.3.3 Selection Pressure -- 4.4 Reproduction
4.4.1 Mutation -- 4.4.2 Recombination -- References -- 5 Popular Evolutionary Computation Algorithms -- 5.1 Genetic Algorithm -- 5.1.1 Basic Principle and Framework -- 5.1.2 Applications of Genetic Algorithms -- 5.2 Evolutionary Programming -- 5.2.1 The Emergence of Evolutionary Programming -- 5.2.2 The Classical Evolutionary Programming -- 5.2.2.1 Representation -- 5.2.2.2 Mutation -- 5.2.2.3 Selection -- 5.2.3 Framework and Parameter Settings -- 5.2.4 Recent Advances in Evolutionary Programming -- 5.3 Genetic Programming -- 5.3.1 Introduction -- 5.3.2 Genotype-Phenotype Mapping