Bio-Inspired Algorithms for Fuzzy Rule-Based Systems

In this chapter we focus on three bio-inspired algorithms and their combinations with fuzzy rule based systems. Rule Based systems are widely being used in decision making, control systems and forecasting. In the real world, much of the knowledge is imprecise and ambiguous but fuzzy logic provides for systems better presentations of the knowledge, which is often expressed in terms of the natural language. Fuzzy rule based systems constitute an extension of classical Rule-Based Systems, because they deal with fuzzy rules instead of classical logic rules. Since bio-inspired optimization algorithms have been shown effective in improving the tuning the parameters and learning the Fuzzy Rule Base, our major objective is to represent the application of these algorithms in improving the Knowledge Based Systems with focus on Rule-Base Learning. We first introduce the Evolutionary Computation and Swarm Intelligence topics as a subfield of Computational Intelligence dealing with Combinatorial Optimization problems. Then, three following bio-inspired algorithms, i.e. Genetic Algorithms, Ant Colony Optimization, and Particle Swarm Optimization are explained and their application in improving the knowledge Based Systems are presented.