1 Introduction to Computational Intelligence

1. Computational Intelligence Paradigms
2. Short History
3. Assignments

2 The Artificial Neuron

1. Calculating the Net Input Signal
2. Activation Functions
3. Artificial Neuron Geometry
4. Artificial Neuron Learning
5. Assignments

3 Supervised Learning Neural Networks

1. Neural Network Types
2. Supervised Learning Rules
3. Functioning of Hidden Units
4. Ensemble Neural Networks
5. Assignments

4 Unsupervised Learning Neural Networks

1. Background
2. Hebbian Learning Rule
3. Principal Component Learning Rule
4. Learning Vector Quantizer-I
5. Self Organizing Feature Maps
6. Assignments

5 Radial Basis Function Networks

1. Learning Vector Quantizer-II
2. Radial Basis Function Neural Networks
3. Assignments

6 Reinforcement Learning

1. Learning through Awards
2. Model-Free Reinforcement Learning Model
3. Neural Networks and Reinforcement Learning
4. Assignments

7 Performance Issues (Supervised Learning)

1. Performance Measures
2. Analysis of Performance
3. Performance Factors
4. Assignments

8 Introduction to Evolutionary Computation

1. Generic Evolutionary Algorithm
2. Representation - The Chromosome
3. Initial Popolation
4. Fitness Function
5. Selection
6. Reproduction Operators
7. Stopping Conditions
8. Evolutionary Computation versus Classical Optimization
9. Assignments

9 Genetic Algorithms

1. Canonical Genetic Algorithm
2. Crossover
3. Mutation
4. Control Parameters
5. Genetic Algorithm Variants
6. Advanced Topics
7. Applications
8. Assignments

10 Genetic Programming

1. Tree-Based Representation
2. Initial Population
3. Fitness Function
4. Crossover Operators
5. Mutation Operators
6. Building Block Genetic Programming
7. Applications
8. Assignments

11 Evolutionary Programming

1. Basic Evolutionary Programming
2. Evolutionary Programming Operators
3. Strategy Parameters
4. Evolutionary Programming Implementations
5. Advanced Topics
6. Applications
7. Assignments

12 Evolution Strategies

1. (1 + 1)-ES
2. Generic Evolutionary Strategy Algorithm
3. Strategy Parameters and Self-Adaptation
4. Evolutionary Strategy Operators
5. Evolutionary Strategy Variants
6. Advanced Topics
7. Applications of Evolutionary Strategies
8. Assignments

13 Differential Evolution

1. Basic Differential Evolution
2. DE-x-y-z
3. Variations to Basic Differential Evolution
4. Differential Evolution for Discrete-Valued Problems
5. Advanced Topics
6. Applications
7. Assignments

14 Cultural Algorithms

1. Culture and Artificial Culture
2. Basic Cultural Algorithm
3. Belief Space
4. Fuzzy Cultural Algorithms
5. Advanced Topics
6. Applications
7. Assignments

15 Coevolution

1. Coevolution Types
2. Competitive Coevolution
3. Cooperative Coevolution
4. Assignments

16 Particle Swarm Optimization

1. Basic Particle Swarm Optimization
2. Social Network Structures
3. Basic Variations
4. Basic PSO Parameters
5. Single-Solution Particle Swarm Optimization
6. Advanced Topics
7. Applications
8. Assignments

17 Ant Algorithms

1. Ant Colony Optimization Meta-Heuristic
2. Cemetery Organization and Brood Care
3. Division of Labor
4. Advanced Topics
5. Applications
6. Assignments

18 Natural Immune Systems

1. Classical View
2. Antibodies and Antigens
3. The White Cells
4. Immunity Types
5. Learning the Antigen Structure
6. The Network Theory
7. The Danger Theory
8. Assignments

19 Artificial Immune Models

1. Artificial Immune System Algorithm
2. Classical View Models
3. Clonal Selection Theoty Models
4. Network Theory Models
5. Danger Theory Models
6. Applications and Other AIS Models
7. Assignments

20 Fuzzy Sets

1. Formal Definitions
2. Membership Functions
3. Fuzzy Operators
4. Fuzzy Set Characteristics
5. Fuzziness and Probability
6. Assignments

21 Fuzzy Logic and Reasoning

1. Fuzzy Logic
2. Fuzzy Inferencing
3. Assignments

22 Fuzzy Controllers

1. Components of Fuzzy Controllers
2. Fuzzy Controller Types
3. Assignments

23 Rough Sets

1. Concept of Discernability
2. Vagueness in Rough Sets
3. Uncertainty in Rough Sets
4. Assignments

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

1. A.5.1 Problem Definition
2. A.5.2 Optimization Algorithms
3. A.5.3 Example Benchmark Function

A.6 Constrained Optimization

1. A.6.1 Problem Definition
2. A.6.2 Constraint Handling Methods
3. A.6.3 Example Benchmark Problems

A.7 Muti-Solution Problems

1. A.7.1 Problem Definition
2. A.7.2 Niching Algorithm Categories
3. A.7.3 Example Benchmark Problems

A.8 Multi-Objective Optimization

1. A.8.1 Multi-Objective Problem
2. A.8.2 Weighted Aggregation Methods
3. A.8.3 Pareto-Optimality

A.9 Dynamic Optimization Problems

1. A.9.1 Definition
2. A.9.2 Dynamic Environment Types
3. A.9.3 Example Benchmark Problems