Sequential and distributed evolutionary algorithms for combinatorial optimization problems

This chapter compares the performance of six evolutionary algorithms, three sequential and three parallel, for solving combinatorial optimization problems. In particular, a generational, a steady-state, a cellular genetic algorithm, and their distributed versions were applied to the maximum cut problem, the error correcting code design problem, and the minimum tardy task problem. The algorithms were tested on a total of seven problem instances. The results obtained in this chapter 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. Just the intrinsic search features of each class of algorithms proved to be powerful enough to solve a given problem instance. Some of the sequential, and almost every parallel algorithm, yielded fast and accurate results, although they sampled only a tiny fraction of the search space.