Abstract
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
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Electronic Commerce Studies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
