Superior-in-Status Analysis of Improved Genetic Algorithm for GTSP

摘要

Superiority in status relation (λ) can be used to rank the given EAs in terms of convergence capacity. The performance of an EA can be improved if it is modified to be superior in status to its original version. In this paper, the λ relation model is applied to analyzing the improvement of generalize-chromosome genetic algorithm (GCGA) for generalized traveling salesman problem (GTSP). Hybrid-chromosome genetic algorithm (HCGA) is superiority to GCGA. The numerical results also indicate that HCGA performs better and more steadied than GCGA in solving several GTSP instances. The case is the application example of the proposed relation model.