Improving Performance of The Genetic Algorithm on NP-Complete Problem

Abstract

Non-Deterministic Polynomial Complete Problem is the most challenging problem and also engaging in algorithm strategy. One representation of this problem is the sudoku numbers game. To fill an empty sudoku puzzle, a specific formula does not apply, but filling in sudoku is a matter of decision. So it takes a special algorithm and strategy to solve it. As such, the case of the sudoku numbers game has been widely praised as the topic of finding the best results. One of the methods used is a genetic algorithm. However, due to many processes and data used in the implementation of genetic algorithms, the results obtained are often not optimal. This research will introduce a special strategy in implementing genetic algorithms in NP-Complete problems, namely by optimizing the genetic algorithm in the process of population formation. From the test results, it is found that the application of the genetic algorithm with optimization results in smaller time data and test data compared to the algorithm without optimization.