The performance evaluation of a hybrid immune genetic algorithm based on mathematical functions

This paper demonstrates a hybrid between two optimization methods that are Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. The key idea of this research is applying negative selection which is a technique in AIS to reduce the number of initial chromosomes and increase strong fitness to a local search space. In addition, the author of this paper has also compared the differences between the minimum fitness values of the testing functions, five mathematical test functions were used to make comparisons. The results from GA, AIS, and PSO illustrated that the IGA produced good quality solutions and outperforms similar methods.