Artificial Chromosomes Embedded in Sub-population Gemetic Algorithm for a Multi-objective Scheduling Problems

Sub-population Genetic Algorithms is a population-based approach for heuristic search in multiple objectives optimization problems. Different from the single objective problem, sub-population genetic algorithms is used to find the Pareto solutions of different objectives. However, the traditional mechanic in the genetic algorithms will diminish the searching space while evolving; it will cause the solutions converging too fast and fall into the local optima. In this research, two different kind of artificial chromosome operators will be introduced when the algorithm evolves to certain iteration for injecting to individual to search better combination of chromosomes, this mechanism will provide a more expansive searching space while evolving. The experiments result shows that these two operators possess fast convergence and average scatter of Pareto solutions simultaneously for solving multi-objective scheduling problems in test instances.