Analysis of distributed genetic algorithms for solving cutting problems

In this paper, a solution to the three-stage two-dimensional cutting problem is presented by using sequential and parallel genetic algorithms (GAs). More specifically, an analysis of including distributed population ideas and parallelism in the basic GA is carried out to solve the problem more accurately and efficiently than with ordinary sequential techniques. Publicly available test problems have been used to illustrate the computational performance of the resulting metaheuristics. Experimental evidence in this work will show that the proposed algorithms outperform their sequential counterparts in time (high speedup with multiprocessors) and numerically (lower number of visited points during the search to find the solutions).