Abstract
We are interested in improving the performance of genetic algorithm (GA) to solve a combinatirial optimization problem. Several approaches have been developed based on the adaptation and improvement of different standard genetic operators. However, GA also has some significant drawbacks, for instance, the premature convergence of computations, expensive computation from evolutional procedures, and the poor capability of local search. Artificial immune system is a class of computational intelligence methods drawing inspiration from human immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. In this paper, an immune based genetic algorithms are proposed to overcome these inconvenients for traveling salesman problem us a typical combinatirial optimization problem. Numerical results are presented for different standard instances from the TSPlib showing the performance of the proposed algorithms.
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Lahjouji El Idrissi, A., Tajani, C., Krkri, I., Fakhouri, H. (2019). Immune Based Genetic Algorithm to Solve a Combinatorial Optimization Problem: Application to Traveling Salesman Problem. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_82
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DOI: https://doi.org/10.1007/978-3-030-11928-7_82
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