Applying evolutionary algorithms to combinatorial optimization problems

Abstract

The paper describes the comparison of three evolutionary algorithms for solving combinatorial optimization problems. In particular, a generational, a steady-state and a cellular genetic algorithm were applied to the maximum cut problem, the error correcting code design problem, and the minimum tardy task problem. The results obtained in this work are better than the ones previously reported in the literature in all cases except for one problem instance. The high quality results were achieved although no problem-specific changes of the evolutionary algorithms were made other than in the fitness function. The constraints for the minimum tardy task problem were taken into account by incorporating a graded penalty term into the fitness function. The generational and steady-state algorithms yielded very good results although they sampled only a tiny fraction of the search space.