Abstract
In the special issue on computational intelligence (CI) and complex systems a plethora of approaches is presented on how to apply computation intelligence in modeling, control, decision support, and optimization of complex problems. The three main components of CI are fuzzy systems, evolutionary and population based algorithms, and artificial neural networks. In addition, there are other related techniques, among others, chaos theory and subjective probability. Often, the combinations and hybrids of these methods, sometimes complemented by classic mathematical or modeling tools, turn out to be most efficient in the solution of real life problems. In the series of 26 papers, illustrations for almost all of these methods may be found...
Highlights
What is the main goal and target in these research problems? Several years ago the Lead Guest Editor published a study on how to use fuzzy rule based systems as tools in solving what was called the “Key Problem of Engineering,” even though “Key Problem of Engineering” is not a term accepted by consensus in the relevant literature
In the present special issue, Engineering may be replaced by the concepts “Applied Problems,” “Real Life Applications,” or similar
E behavior of this kind of models was addressed in a form reduced to optimizing fuzzy rule based models from the point of view of practical applicability, which was called “Fuzzy Cat and Mouse Problem”: a theoretical experiment where the “fuzzy cat” was the implementation of a simple computational intelligence (CI) approach
Summary
What is the main goal and target in these research problems? Several years ago the Lead Guest Editor published a study on how to use fuzzy rule based systems as tools in solving what was called (maybe, in a somewhat exaggerating way) the “Key Problem of Engineering,” even though “Key Problem of Engineering” is not a term accepted by consensus in the relevant literature. In the special issue on computational intelligence (CI) and complex systems a plethora of approaches is presented on how to apply computation intelligence in modeling, control, decision support, and optimization of complex problems.
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