本书分两部分，第一部分介绍基本的智能优化方法，包括传统的启发式搜索算法以及以演化算法为代表的群智能搜索方法；第二部分介绍演化优化领域常见的优化问题，包括多模优化，多目标优化，约束优化，动态优化，鲁棒优化等，以及实际生产生活中的优化实例。本书知识点新，展现智能优化的发展过程与趋势，选用了当前该领域近期新的研究成果。

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 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
2.3 Computational Complexity
2.3.1 Complexity Measures
2.3.2 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
3.2.3 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 Neighborhood 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
4.2.3 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 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 and Framework
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
5.2.3 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
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
6.2.3 Discussions
7 Exploitation versus Explorat