This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced third edition has been fully revised and expanded with new content on deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms. 

Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

Table of Contents

1 Introduction

Part I Neural Networks

2 Introduction to Artificial Neural Networks

3 Threshold Logic Units

4 General Neural Networks

5 Multi-layer Perceptrons

6 Radial Basis Function Networks

7 Self-organizing Maps

8 Hopfield Networks

9 Recurrent Networks

10 Neural Networks: Mathemat ical Remarks

Part II Evolutionary Algorithms

11 Introduction to Evolutionary Algorithms

12 Elements of Evolutionary Algorithms

13 Fundamental Evolutionary Algorithms

14 Computational Swarm Intelligence

Part Ill Fuzzy Systems

15 Introduction to Fuzzy Sets and Fuzzy Logics

16 The Extension Principle

17 Fuzzy Relations

18 Similarity Relations

19 Approximate Reasoning

20 Fuzzy Control

21 Hybrid Systems for Tuning and Learning Fuzzy Systems

22 Fuzzy Data Analysis

Part IV Bayes and Markov Networks

23 Bayesian Networks

24 Elements of Probability and Graph Theory

25 Decompositions

26 Evidence Propagation

27 Learning Graphical Models

28 Belief Revision

29 Decision Graphs

30 Causal Networks

About the Authors

Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Sanaz Mostaghim is a full Professor of Computer Science and Christian Braune is a Senior Lecturer at the same institution. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.