ABSTRACT
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
In the past decade, an evolution has been witnessed in some of the major information processing techniques which are not all necessarily analytical types. Specifically fuzzy logic, artificial neural networks, and evolutionary algorithms have witnessed major transformations. The key ideas of these algorithms have their roots in the 1960s. As years passed, these algorithms developed based on the needs to solve the problems. In the 1990s the need of integrating different algorithms came into existence. Most importantly, the need was to develop a more powerful tool which would combine strengths of all the existing algorithms to solve high complexity problems. At this point of time, soft computing came into existence and was proposed as a synergically fused algorithms tool which promised to yield innovative promising solutions in the future.
