A Dynamic Adaptive Calibration of the CLONALG Immune Algorithm

The control of parameters during the execution of bio-inspired algorithms is an open research area. In this paper, we propose a new parameter control strategy for the immune algorithm CLONALG. Our approach is based on reinforcement learning ideas. We focus our attention on controlling the number of clones and the number of selected cells which follow a mutation process for improvement. Their values allow a trade-off between intensification and diversification of the search. Our approach provides an efficient and low cost adaptive technique for parameter control. We use instances of the Travelling Salesman Problem that has been tackled before by using CLONALG. The results obtained are very encouraging.