摘要：In this paper, we present a new mo del of an ar-tificial immune system (AIS), based on the pro-cess that su.ers the T-Cell, it is called T-CellModel. It is used for solving constrained (nu-merical) optimization problems. The model op er-ates on three p opulations: Virgins, E.ectors andMemory. Each of them has a di.erent role. Also,the mo del dynamically adapts the tolerance fac-tor in order to improve the exploration capabil-ities of the algorithm. We also develop a newmutation operator which incorporates knowledgeof the problem. We validate our proposed ap-proach with a set of test functions taken fromthe sp ecialized literature and we compare our re-sults with respect to Sto chastic Ranking (whichis an approach representative of the state-of-the-art in the area), with respect to an AIS pre-viously prop osed and a self-organizing migrat-ing genetic algorithm for constrained optimiza-tion (C-SOMGA)
关键词：Artificial Immune System; Con-;strained Optimization Problem
