Abstract
The Multi-Offspring Genetic Algorithm (MOGA) is a GA variant that was proposed specifically for the Traveling Salesman Problem (TSP). Like the Base GA (BGA), the first genetic algorithm designed to solve the TSP, MOGA has a crossover operator which is non trivial to implement. This paper proposes a copy and paste crossover operator for the multi-offsprings genetic algorithm which is easy to implement and also effective in generating a family of diverse offsprings. The algorithm named Copy and Paste Multi-offspring Genetic Algorithm (CP-MOGA) from the crossover operator design. Crossover and mutation in CP-MOGA is designed to cater for exploration and exploitation by carefully choosing a gene insertion section and mutation point such that two parents produce two predominantly exploratory, two predominantly exploitative and two moderately exploratory and exploitative offsprings, thereby balancing the exploration exploitation trade-off. Simulation results on twelve instances of the Traveling Salesman Problem show that the proposed algorithm outperforms MOGA and BGA in most cases.
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Acknowledgment
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71901152, 71971143), Guangdong innovation team project “intelligent management and cross innovation” (2021WCXTD002), Scientific Research Team Project of Shenzhen Institute of Information Technology (SZIIT2019KJ022), and Guangdong Basic and Applied Basic Research Foundation (Project No. 2019A1515011392).
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Tungom, C.E., Niu, B., Xing, T., Yuan, J., Wang, H. (2022). Copy and Paste: A Multi-offspring Genetic Algorithm Crossover Operator for the Traveling Salesman Problem. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_23
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