醉染图书智能优化(英文版)9787562550

Part I The Basics
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
2 Fundamentals
2.1 Fitness Landscapes
2.1.1 Solution Space
2.1.2 Objective Space
2.1.3 Neiourhood
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.. Deceptiveness
2.2.4 Neutrality
2.2.5 Separability
2.2.6 Scalability
2.2.7 Domino convergence
2.2.8 Property Control
. Computational Complexity
..1 Complexity Measures
..2 P Versus NP Problem
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
3.2.2 Uninformed Search
3.. Informed 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 Neiorhood Search
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.. 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 Age-based Replacement
4.3.4 Fitness-based Replacement
4.3.5 Selection Pressure
4.4 Reproduction
4.4.1 Mutation
4.4.2 Recombination
5 Popular Evolutionary Computation Algorithms
5.1 Genetic Algorithms
5.1.1 Basic Principle an Faework
5.1.2 Applications of Genetic Algorithms
5.2 Evolutionary Programming
5.2.1 The Emerging of Evolutionary Programming
5.2.2 The Classical Evolutionary Programming
5.. 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
5.3.3 Other Genome Structures
5.3.4 Open Issues
5.4 Particle Swarm Optimization
5.4.1 The Arising of Particle Swarm Optimization
5.4.2 Original Particle Swarm Optimization
5.4.3 Standard Particle Swarm Optimization
5.4.4 Recent Advances in Particle Swarm Optimization
5.5 Differential Evolution
5.5.1 Introduction of Differential Evolution
5.5.2 Framework and Parameter Settings
5.5.3 Some Advances in Differential Evolution
5.6 Evolution Strategy
5.6.1 Basic Evolution Strategy Paradigm
5.6.2 Covariance Matrix Adaptation Evolution Strategy
5.7 Estimation of Distribution Algorithm
5.7.1 Standard Procedures
5.7.2 Discrete Versions
5.7.3 Continuous Versions
5.8 Ant Colony Optimization
5.8.1 Biological Inspiration
5.8.2 ACO framework
5.8.3 ACO Variants
5.8.4 Recent Advances
6 Parameter Control and Policy Control
6.1 Parameter Control
6.1.1 Unary Parameter Control
6.1.2 Multi-parameter Control
6.1.3 Discussions
6.2 Policy Control
6.2.1 Operator Selection Control
6.2.2 Hyper-heuristics
6.. Discussions
7 Exploitation versus Explorat