Oklahoma State University (OSU) researchers have published a study on the use of artificial immune systems (AIS) in evolutionary algorithms. By copying the way a living body acquires immunity to disease through vaccination, researchers have designed an AIS to more efficiently solve optimization problems. The results show that the biologically motivated approach is better at exploring a greater amount of space than previous methods. Unlike previous forms of AIS, the OSU system capitalizes on the way that vaccines can improve the performance of the immune system. Vaccines enable immune systems to detect new, weakened antigens and develop a biological memory so they can recognize the same antigen in the future. The researchers drew inspiration from how vaccines work in designing the new AIS. They inject the AIS with certain points in the decision space that act as a weak antigen, or vaccine. When comparing the new algorithm, called Vaccine-AIS, to other types of AIS, the researchers found that Vaccine AIS outperformed the others by locating the optimum point in a plot in fewer evaluations. “AIS was originally designed for data mining, anomaly detection, and the like,” says OSU’s Gary Yen. “Its use as an optimization tool is a very young research area but its performance is drawing interest from researchers.”

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