Paralleled Genetic Algorithm for Solving the Knapsack Problem in the Cloud

This paper proposes a Parallel Genetic Algorithm (PGA) framework to solve the Knapsack problem in the Cloud. Our PGA consists of several independent workers that cooperatively run in parallel to find optimal solutions for a Knapsack problem, we chose the Knapsack problem because it is known to be NP-Complete and can be used to motivate other cloud-based solutions for other combinatorial problems too. Although this problem has already been extensively studied in the literature, no cloud-based solution is ever presented for that -- to the best of our knowledge. We used several benchmarks to validate our solutions against the optimal solution computed by dynamic programming. Results are very promising and illustrated reasonable scalability as well as speed up factor for our implementation using Microsoft Azure.