
Computational Intelligence:
Table of Contents 

Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 
Chapter 1 Background presents definitions of
terms that will be used throughout the book. It briefly reviews biological
and behavioral motivations for the constituent methodologies of
computational intelligence. The major application areas for each of the
constituent methodologies, as well as of computational intelligence, are
briefly reviewed. Chapter 2 Computational Intelligence, launches the reader directly into the core subject of the book. Rather than wait until evolutionary computation, neural networks, and fuzzy systems are all presented and discussed, and we pull them all together, we have chosen to immerse the reader directly into the computational intelligence milieu. We first review the concepts of adaptation and selforganization, key to our view of computational intelligence. Then we summarize the brief history of the computational intelligence field, viewing it from the perspectives of other researchers. This leads us into a discussion of our view of the relationships among the three major components and how they cooperate and/or are integrated into a computational intelligence system. We present our definition of computational intelligence, supported by diagrams that place it into context.
The next section of the book comprises backbone chapters on each of the
three main constituents of computational intelligence: evolutionary
computation, neural networks, and fuzzy logic, in that order. This order
provides an initial focus on evolutionary computation, which is presented as
providing a foundation for development of computational intelligence tools
involving neural networks and fuzzy logic. When neural networks are
discussed, we see how evolutionary computation can be used to evolve the
weights and structure of feedforward neural networks. When we get to fuzzy
logic, we examine evolutionary computation applications to tools built using
fuzzy logic. In other words, the evolutionary computation theme pervades the
book.
Chapter 3  Evolutionary Computation, has been
adapted from the Evolutionary Computation Theory and Paradigms chapter in
Swarm Intelligence (Kennedy, Eberhart and Shi, 2001) with updates and
augmentations, including recent developments in particle swarm optimization
and other evolutionary computation approaches. After reviewing the history
of evolutionary computation, and giving an overview of the field, we discuss
each of the main paradigms of evolutionary computation: genetic algorithms,
evolutionary programming, evolution strategies, genetic programming, and
particle swarm optimization.
Chapter 4 Evolutionary Computation Implementations,
discusses issues to be considered when implementing evolutionary computation
paradigms, and presents two implementation examples: a "plain vanilla"
genetic algorithm, and the realvalued particle swarm. The particle swarm
optimization implementation can be run as a single particle swarm, or in a
multiswarm mode.
Chapter 5 Artificial Neural Networks, first
briefly overviews the history of neural networks, then examines what they
are and why they are useful. Next is a discussion of neural network
components and terminology, and a review of neural network topologies. A
more detailed look at neural network learning and recall follows, focusing
on three of the most commonly used neural network paradigms: backpropagation,
learning vector quantization, and selforganizing feature map networks.
These networks also represent the two basic learning types, supervised
learning (backpropagation) and unsupervised learning (learning vector
quantization and selforganizing feature maps). Hybrid networks and
recurrent networks are briefly discussed. Finally, the issues of
preprocessing and postprocessing are examined.
Chapter 6 Neural Network Implementations,
discusses issues to be considered when implementing artificial neural
networks, and presents four implementation examples: backpropagation,
learning vector quantization, selforganizing feature maps, and evolutionary
neural networks.
Chapter 7 Fuzzy Systems, Fuzzy Systems, leads
off with a brief review of the history of the field, followed by an
examination of fuzzy sets and fuzzy logic, the concepts of fuzzy sets, and
approximate reasoning. The differences between fuzzy logic and probability
are stressed. Both Mamdani and TakagiSugeno approaches to the design and
analysis of fuzzy systems are presented. The chapter concludes by looking at
some issues and special topics related to fuzzy systems.
Chapter 8 Fuzzy System Implementations,
discusses issues to be considered when implementing fuzzy systems, and
presents two implementation examples: a traditional fuzzy rule system, and
an evolutionary fuzzy system. It is with the evolutionary fuzzy system that
we begin the transition into computational intelligence systems.
Chapter 9 Computational Intelligence
Implementations, reflects recent developments in the field, including
evolutionary fuzzy systems and approaches to system adaptation using
computational intelligence. The interaction and cooperation among the three
basic components of computational intelligence will be expanded and a
section on adaptive evolutionary computation using fuzzy systems and/or
neural networks will be included. The role of nonlinear dynamics (chaos
theory) in computational intelligence is emphasized. Included will be
material on recent developments in "edge of chaos" research and development
that focuses on complex systems.
Chapter 10 Performance Metrics, includes a
number of system performance measures not generally used in other
disciplines. Included are percent correct, sumsquared error, absolute
error, normalized error, receiver operating characteristic curves. recall
and precision, confusion matrices, and the chisquared test.
Chapter 11 Analysis and Explanation, presents
several tools that are helpful in assessing and explaining how well a
computational intelligence tool is doing its job. Included are sensitivity
analyses, Hinton diagrams for neural networks, and the use of evolutionary
computing tools for analysis. An example of using particle swarm to develop
an explanation facility is included in this chapter.
Chapter 12 Case Study Summaries, provides
examples of practical applications. Two case studies are based on recent
work by the authors, including one on human tremor analysis and another on
optimization of logistics operations. Other case studies discussed in detail
are schedule optimization and control system design. Several other case
study examples are briefly reviewed. Software and datasets that support
selected case studies are on the book's web site. 



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