Hybridizing an immune artificial algorithm with epsilon constrained method

In this paper, we present a modified version of an algorithm inspired on the T-Cell model, it is an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed algorithm is called TCEC (T-Cell Epsilon Constrained) due to it is increased with epsilon constrained method, for solving constrained (numerical) opti- mization problems. We validate our proposed approach with a set of 36 test functions provided for the CEC 2010 competition. We indirectly compare our results with respect to a version of the differential evolution algorithm. Our results show that TCEC can found feasible solutions on almost test functions with 10 and 30 decision variables. Eje: Workshop Agentes y sistemas inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI)