Computational Intelligence: An Introduction, 2/e (Hardcover)

Description 

The main focus of the book is on computational modelling of biological and natural intelligent systems in order to develop nature inspired artificially intelligent systems. These algorithmic models have as their main objective to facilitate the implementation of artificial intelligent systems for solving complex real-world systems (e.g. fuzzy systems are applied successfully to control systems, gear transmission, breaking systems; swarm intelligence to image classification).

This second edition expands on all these paradigms, providing a more detailed and equal treatment of them all. Most recent advances in CI have been added, namely artificial immune systems, hybrid systems, and a section on how to perform empirical studies.

Table of Contents

Page

List of Tables

List of Figures

List of Algorithms

Preface

Part I INTRODUCTION

1 Introduction to Computational Intelligence

Part II ARTIFICIAL NEURAL NETWORKS

2 The Artificial Neuron 

3 Supervised Learning Neural Networks

4 Unsupervised Learning Neural Networks

5 Radial Basis Function Networks 

6 Reinforcement Learning

7 Performance Issues (Supervised Learning)

Part III EVOLUTIONARY COMPUTATION

8 Introduction to Evolutionary Computation

9 Genetic Algorithms

10 Genetic Programming

11 Evolutionary Programming

12 Evolution Strategies

13 Differential Evolution

14 Cultural Algorithms

15 Coevolution

Part IV COMPUTATIONAL SWARM INTELLIGENCE

16 Particle Swarm Optimization

17 Ant Algorithms

Part V ARTIFICIAL IMMUNE SYSTEMS

18 Natural Immune System

19 Artificial Immune Models

Part VI FUZZY SYSTEMS

20 Fuzzy Sets

21 Fuzzy Logic and Reasoning

22 Fuzzy Controllers

23 Rough Sets

24 FINAL REMARKS

References

A Optimization Theory