Solving NP-Complete Problems on the CUDA Architecture Using Genetic Algorithms

This paper focuses on solutions to two NP-Complete problems: k-SAT and the knapsack problem. We propose a new parallel genetic algorithm strategy on the CUDA architecture, and perform experiments to compare it with the sequential versions. We show how these problems can benefit from the GPU solutions, leading to significant improvements in speedup while keeping the quality of the solution. The best performance obtained in terms of speedup is 67 times. The solution presented in this paper suggests a general strategy for finding fast and robust solutions to complex problems.