Genetic algorithms for engineering optimization: Theory and practice

The genetic algorithms are heuristics and thus they do not ensure an optimal solution. We propose to use a fuzzy controller for improvement of genetic algorithms. A speed of natural evolution is changeable. Genetic algorithms can be classified into three main categories: a basic GA; evolution strategies; a mobile GA. The mobile GA has a variable chromosome structure. The aim of this paper is to consider a efficiency of various GAs. The paper will explore the utility of the recently developed GA paradigm for model fitting using sets of empirical data. To support this work, the real-world problems has been explored The examples of real-world problems are telecommunication networks traffic optimization and task of elements placement on plane. In case telecommunication network traffic optimization, a fitting model is a fuzzy rule based system. In this paper, a concept of fuzzy probabilistic variable is introduced.