The Ph.D. thesis presents a newly designed novel Genetic Algorithm namely, CAscaded Genetic Algorithm (CAGA). The ultimate goal of the method is to provide the enhancement of the conventional GAs. CAGA eliminates the possibility to be trapped in a local optimum which is the primary drawback of the conventional GA. The hybrid version of CAGA has very well demonstrates this criterion. The cascaded method has been applied on data clustering, function optimization and shape recognition. The results of CAGA shows that it outperforms or equally performs compared to the relevant genetic algorithms.