Abstract
We present a hybrid genetic algorithm that incorporates the Generalized Partition Crossover (GPX) operator to produce an algorithm that is competitive with the state of the art for the Traveling Salesman Problem (TSP). GPX is respectful, transmits alleles and is capable of tunneling directly to new local optima. Our results show that the hybrid genetic algorithm quickly finds optimal and near optimal solution on problems ranging from 500 to 1817 cities using a population size of 10. It is also superior to Chained-LK given similar computational effort. Additional analysis shows that all the edges found in the globally optimal solution are present in a population after only a few generations in almost every run. Furthermore the number of unique edges in the population is also less than twice the problem size.
This effort was sponsored by the Air Force Office of Scientific Research, Air Force Materiel Command, USAF, under grant number FA9550-08-1-0422. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.
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Whitley, D., Hains, D., Howe, A. (2010). A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_57
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DOI: https://doi.org/10.1007/978-3-642-15844-5_57
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