During the last decade the human behaviour and human imitating processing methods have become of central interest through the scientific community. The development of methods that mimic the human learning process being able to solve complex engineering problems which are difficult to deal with via conventional approaches, seems to be on an immediate emergency. Concepts such as nervous system, fuzziness and evolution come directly from human resources enclosing attractive properties and reach theory, and as a consequence lead to new scientific horizons. In this direction, soft computing indicates a new family of computing techniques that accommodate human computing resources and make them being utilized. Neural networks, fuzzy systems and genetic algorithms are mainly the three basic constituents that contribute to this juncture. Starting with the basic features in each one of these partners, this paper is focused on the examination of all the possible combined (hybrid) methods among these units providing their main characteristics under a critical aspect. Moreover, a variety of engineering applications is presented demonstrating the enormous field of action that soft computing surrounds, as well as proving the importance of dealing with hybrid intelligent methods.
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