Hybridizing a genetic algorithm with an artificial immune system for global optimization

This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.