Genetic algorithm for Traveling Salesman Problem-Reference-Cited by-同舟云学术

Genetic algorithm for Traveling Salesman Problem

Published:2022-11-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems
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Author:

Xu Haojie¹^ORCID,Ge Yisu²^ORCID,Zhang Guodao³^ORCID

Affiliation:

1. Wencheng Electric Power Supply Company, State Grid Corporation of China, China

2. College of Computer science and Artificial intelligence, Wenzhou University, China

3. Department of Digital Media Technology, Hangzhou Dianzi University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581792.3581798

Reference36 articles.

1. Akter , S. , N. Nahar , M. ShahadatHossain and K. Andersson ( 2019 ). A new crossover technique to improve genetic algorithm and its application to TSP . 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE. Akter, S., N. Nahar, M. ShahadatHossain and K. Andersson (2019). A new crossover technique to improve genetic algorithm and its application to TSP. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE.

2. Ant colony versus genetic algorithm based on travelling salesman problem;Alhanjouri M. A.;Int. J. Comput. Tech. Appl,2013

3. Baluja , S. and R. Caruana ( 1995 ). Removing the genetics from the standard genetic algorithm . Machine Learning Proceedings 1995, Elsevier : 38 - 46 . Baluja, S. and R. Caruana (1995). Removing the genetics from the standard genetic algorithm. Machine Learning Proceedings 1995, Elsevier: 38-46.

4. Distributed individuals for multiple peaks: A novel differential evolution for multimodal optimization problems;Chen Z.;IEEE Transactions on Evolutionary Computation,2019

5. Davis , L. ( 1985 ). Applying adaptive algorithms to epistatic domains. IJCAI . Davis, L. (1985). Applying adaptive algorithms to epistatic domains. IJCAI.