Abstract
The Germinal center artificial immune system (GC-AIS) is a novel immune algorithm inspired by recent research in immunology, which requires very few parameters to be set by hand. The population of solutions in GC-AIS is dynamic in nature and has no restrictions on its size which can cause problems of population explosion, where the population keeps growing very rapidly, leading to wasteful fitness evaluations. In this paper we try to address this problem in the GC-AIS by incorporating \(\epsilon \)-dominance, which is a well known mechanism in multi-objective optimization to regulate population size. The improved variant of GC-AIS is compared with a well known multi-objective evolutionary algorithm NSGA-II on the multi-objective knapsack problem. We show that our improved GC-AIS performs better than NSGA-II on the instances of the knapsack problem taken from [23] inheriting the same benefits of having to set fewer parameters manually.
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Joshi, A., Rowe, J.E., Zarges, C. (2015). Improving the Performance of the Germinal Center Artificial Immune System Using \(\epsilon \)-Dominance: A Multi-objective Knapsack Problem Case Study. In: Ochoa, G., Chicano, F. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2015. Lecture Notes in Computer Science(), vol 9026. Springer, Cham. https://doi.org/10.1007/978-3-319-16468-7_10
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DOI: https://doi.org/10.1007/978-3-319-16468-7_10
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