(In)Complete Table of ContentsPart I Introduction [ Show/Hide ]1 Introduction to Computational Intelligence
- Computational Intelligence Paradigms
- Short History
- Assignments
Part II Artificial Neural Networks [ Show/Hide ]2 The Artificial Neuron
- Calculating the Net Input Signal
- Activation Functions
- Artificial Neuron Geometry
- Artificial Neuron Learning
- Assignments
3 Supervised Learning Neural Networks
- Neural Network Types
- Supervised Learning Rules
- Functioning of Hidden Units
- Ensemble Neural Networks
- Assignments
4 Unsupervised Learning Neural Networks
- Background
- Hebbian Learning Rule
- Principal Component Learning Rule
- Learning Vector Quantizer-I
- Self Organizing Feature Maps
- Assignments
5 Radial Basis Function Networks
- Learning Vector Quantizer-II
- Radial Basis Function Neural Networks
- Assignments
6 Reinforcement Learning
- Learning through Awards
- Model-Free Reinforcement Learning Model
- Neural Networks and Reinforcement Learning
- Assignments
7 Performance Issues (Supervised Learning)
- Performance Measures
- Analysis of Performance
- Performance Factors
- Assignments
Part III EVOLUTIONARY COMPUTATION [ Show/Hide ]8 Introduction to Evolutionary Computation
- Generic Evolutionary Algorithm
- Representation - The Chromosome
- Initial Popolation
- Fitness Function
- Selection
- Reproduction Operators
- Stopping Conditions
- Evolutionary Computation versus Classical Optimization
- Assignments
9 Genetic Algorithms
- Canonical Genetic Algorithm
- Crossover
- Mutation
- Control Parameters
- Genetic Algorithm Variants
- Advanced Topics
- Applications
- Assignments
10 Genetic Programming
- Tree-Based Representation
- Initial Population
- Fitness Function
- Crossover Operators
- Mutation Operators
- Building Block Genetic Programming
- Applications
- Assignments
11 Evolutionary Programming
- Basic Evolutionary Programming
- Evolutionary Programming Operators
- Strategy Parameters
- Evolutionary Programming Implementations
- Advanced Topics
- Applications
- Assignments
12 Evolution Strategies
- (1 + 1)-ES
- Generic Evolutionary Strategy Algorithm
- Strategy Parameters and Self-Adaptation
- Evolutionary Strategy Operators
- Evolutionary Strategy Variants
- Advanced Topics
- Applications of Evolutionary Strategies
- Assignments
13 Differential Evolution
- Basic Differential Evolution
- DE-x-y-z
- Variations to Basic Differential Evolution
- Differential Evolution for Discrete-Valued Problems
- Advanced Topics
- Applications
- Assignments
14 Cultural Algorithms
- Culture and Artificial Culture
- Basic Cultural Algorithm
- Belief Space
- Fuzzy Cultural Algorithms
- Advanced Topics
- Applications
- Assignments
15 Coevolution
- Coevolution Types
- Competitive Coevolution
- Cooperative Coevolution
- Assignments
Part IV COMPUTATIONAL SWARM INTELLIGENCE [ Show/Hide ]16 Particle Swarm Optimization
- Basic Particle Swarm Optimization
- Social Network Structures
- Basic Variations
- Basic PSO Parameters
- Single-Solution Particle Swarm Optimization
- Advanced Topics
- Applications
- Assignments
17 Ant Algorithms
- Ant Colony Optimization Meta-Heuristic
- Cemetery Organization and Brood Care
- Division of Labor
- Advanced Topics
- Applications
- Assignments
Part V ARTIFICIAL IMMUNE SYSTEMS [ Show/Hide ]18 Natural Immune Systems
- Classical View
- Antibodies and Antigens
- The White Cells
- Immunity Types
- Learning the Antigen Structure
- The Network Theory
- The Danger Theory
- Assignments
19 Artificial Immune Models
- Artificial Immune System Algorithm
- Classical View Models
- Clonal Selection Theoty Models
- Network Theory Models
- Danger Theory Models
- Applications and Other AIS Models
- Assignments
Part VI FUZZY SYSTEMS [ Show/Hide ]20 Fuzzy Sets
- Formal Definitions
- Membership Functions
- Fuzzy Operators
- Fuzzy Set Characteristics
- Fuzziness and Probability
- Assignments
21 Fuzzy Logic and Reasoning
- Fuzzy Logic
- Fuzzy Inferencing
- Assignments
22 Fuzzy Controllers
- Components of Fuzzy Controllers
- Fuzzy Controller Types
- Assignments
23 Rough Sets
- Concept of Discernability
- Vagueness in Rough Sets
- Uncertainty in Rough Sets
- Assignments
An Optimization Theory [ Show/Hide ]A.1 Basic Ingredients of Optimization Problems A.2 Optimization Problem Classifications A.3 Optima Types A.4 Optimization Method Classes A.5 Unconstrained Optimization
- A.5.1 Problem Definition
- A.5.2 Optimization Algorithms
- A.5.3 Example Benchmark Function
A.6 Constrained Optimization
- A.6.1 Problem Definition
- A.6.2 Constraint Handling Methods
- A.6.3 Example Benchmark Problems
A.7 Muti-Solution Problems
- A.7.1 Problem Definition
- A.7.2 Niching Algorithm Categories
- A.7.3 Example Benchmark Problems
A.8 Multi-Objective Optimization
- A.8.1 Multi-Objective Problem
- A.8.2 Weighted Aggregation Methods
- A.8.3 Pareto-Optimality
A.9 Dynamic Optimization Problems
- A.9.1 Definition
- A.9.2 Dynamic Environment Types
- A.9.3 Example Benchmark Problems
Index |