In this study, the effect of distributions of solution candidates on the problem space in the meta-heuristic search process and the performance of algorithms has been investigated. For this purpose, solution candidates have been created with random and gauss (normal) distributions. Search performance is measured separately for both types of distribution of algorithms. The performances of the algorithms have been tested on the most popular and widely used benchmark problems. Experimental studies have been conducted on the most recent meta-heuristic search algorithms. It has been seen that the search performance of algorithms varies considerably depending on the method of distribution. In fact, better results were obtained than the distribution methods used in the original versions of the algorithms. Algorithms have revealed their abilities in terms of neighborhoods searching, getting rid of local minimum traps and speeding up searches.