Hybrid algorithms incorporated with parallel processing techniques are very powerful tools for efficiently solving very complex optimization problems. We present asynchronous parallel computer architecture adaptation based on hybridization of Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). In this master-slave formulation, slaves perform evolutionary computation independently using GAs, while master supervises and controls the searching process. Master's role is to probabilistically study the characteristics of solution space and directs the slaves on good searching spots. This study reports some few findings on the ability of our hybrid algorithm to solve some instances of BQP problem as well as AODV routing optimization in VANETs. For both problems our hybrid algorithm has obtained best results in terms of quality of solutions as well as computational speed.