Techniques to reduce the search space when an optimizer seeks an optimal value are studied in this paper. A new mutation technique called the “Exponential Moving Average” algorithm (EMA) is introduced. The performance of EMA algorithms is compared to two other similar Computational Intelligence (CI) algorithms (an ordinary Evolutionary Algorithm (EA) and a “Mean-Variance Optimization” (MVO)) to solve a multi-dimensional problem which has a large search space. The classic Sudoku puzzle is chosen as the problem with a large search space.