@article{oai:ipsj.ixsq.nii.ac.jp:00017285,
 author = {HUNGDINHNGUYEN and IKUO, YOSHIHARA and KUNIHITO, YAMAMORI and MORITOSHI, YASUNAGA and Hung, DinhNguyen and Ikuo, Yoshihara and Kunihito, Yamamori and Moritoshi, Yasunaga},
 issue = {SIG10(TOM7)},
 journal = {情報処理学会論文誌数理モデル化と応用（TOM）},
 month = {Nov},
 note = {This paper presents new enhancements to a multi-population GENITOR-type Genetic Al-gorithm (GA)for solving symmetric and asymmetric Traveling Salesman Problems (TSPs). First improvements to the greedy subtour crossover are proposed so that it works more effectively at the stage of highly t individuals.Next local search heuristics are combined with GA to compensate for its lack of local search ability.The powerful Lin-Kernighan heuristic is used for symmetric TSPs and the fast 3-Opt heuristic is used for asymmetric TSPs. Various symmetric and asymmetric TSP benchmarks taken from the TSPLIB are used to validate the method.Experimental results show that the proposed method can nd optimal solutions for problems ranging in size up to 3795 cities in a reasonable computing time. From the viewpoint of quality of solution these results are the best so far obtained by applyingGA to the TSP., This paper presents new enhancements to a multi-population GENITOR-type Genetic Al-gorithm (GA)for solving symmetric and asymmetric Traveling Salesman Problems (TSPs). First,improvements to the greedy subtour crossover are proposed so that it works more effectively at the stage of highly t individuals.Next,local search heuristics are combined with GA to compensate for its lack of local search ability.The powerful Lin-Kernighan heuristic is used for symmetric TSPs and the fast 3-Opt heuristic is used for asymmetric TSPs. Various symmetric and asymmetric TSP benchmarks taken from the TSPLIB are used to validate the method.Experimental results show that the proposed method can nd optimal solutions for problems ranging in size up to 3795 cities in a reasonable computing time. From the viewpoint of quality of solution,these results are the best so far obtained by applyingGA to the TSP.},
 pages = {165--175},
 title = {Greedy Genetic Algorithms for Symmetric and Asymmetric TSPs},
 volume = {43},
 year = {2002}
}