Solving constrained optimization using a t-cell artificial immune system - Aragon, Victoria Soledad (2008)
In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model  is used for solving constrained (numerical)  optimization problems. The  model operates on three populations: Virgins, Effectors and  Memory.  Each of them has a different role. Also, the model dynamically adapts  the tolerance factor in order to improve the exploration capabilities of  the algorithm.  We also develop a new mutation operator which  incorporates knowledge of the problem. We validate our proposed  approach with a set of test functions taken from the specialized  literature and we compare our results with respect to Stochastic  Ranking (which is an approach representative of the state-of-the-art in  the area) and  with respect to an AIS previously proposed.