Offering a wide range of programming examples implemented in
MATLAB(r), Computational Intelligence Paradigms: Theory and
Applications Using MATLAB(r) presents theoretical concepts and a
general framework for computational intelligence (CI) approaches,
including artificial neural networks, fuzzy systems, evolutionary
computation, genetic algorithms and programming, and swarm
intelligence. It covers numerous intelligent computing
methodologies and algorithms used in CI research.
The book first focuses on neural networks, including common
artificial neural networks; neural networks based on data
classification, data association, and data conceptualization; and
real-world applications of neural networks. It then discusses fuzzy
sets, fuzzy rules, applications of fuzzy systems, and different
types of fused neuro-fuzzy systems, before providing MATLAB
illustrations of ANFIS, classification and regression trees, fuzzy
c-means clustering algorithms, fuzzy ART map, and Takagi Sugeno
inference systems. The authors also describe the history,
advantages, and disadvantages of evolutionary computation and
include solved MATLAB programs to illustrate the implementation of
evolutionary computation in various problems. After exploring the
operators and parameters of genetic algorithms, they cover the
steps and MATLAB routines of genetic programming. The final chapter
introduces swarm intelligence and its applications, particle swarm
optimization, and ant colony optimization.
Full of worked examples and end-of-chapter questions, this
comprehensive book explains how to use MATLAB to implement CI
techniques for the solution of biological problems. It will help
readers with their work on evolution dynamics, self-organization,
natural and artificial morphogenesis, emergent collective
behaviors, swarm intelligence, evolutionary strategies, genetic
programming, and the evolution of social behaviors.
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