A Research of Evolutionary Computation for Combinatorial Optimization Problems

II complexity of the algorithm is greatly reduced especially to large scale problems. Applying this application of FSO on large number of instances and comparing with GA, the experiments results shows that these designs could improve the effectiveness, convergence rate and stability for FSO. Since various optimization algorithms take the different speed of operation or evolutionary (iteration) process, it is hard to measure the evolutionary curves against to generation or iteration. In this study most of evolutionary curves are given against to CPU-time. By this way, it is demonstrated clearly for various evolutionary computations to compare with the evolutionary process each other.