This paper proposes a generational model genetic algorithm-based system for solving real-world large scale set partitioning problems (SPP). The SPP is an important combinatorial optimization and has many applications like airline crew scheduling. Two improved genetic algorithm (GA) components are introduced and applied to the generational model GA system that can effectively find feasible solutions for difficult and large scale set partitioning problems. The two components are the grouping crossover operator and a modified local optimizer. The experimental results in this research show that the performance of this GA based system is capable of producing optimal or near-optimal solutions for large scale instances of SPP.
To cite this document: Chi-san Althon Lin, "Generational model genetic algorithm for real world set partitioning problems", International Journal of Electronic Commerce Studies, Vol.4, No.1, pp.33-46, 2013.
Permanent link to this document:
http://dx.doi.org/10.7903/ijecs.1138
