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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