Experimental analyses of evolutionary negative selection algorithm for function optimization

Evolutionary Negative Selection Algorithm is a synthesis of the negative selection mechanism and the evolutionary learning mechanism in biological immune system. In addition to mutation operator and selection operator of traditional Evolutionary Algorithm, negative selection operator will also affect the performance of the Evolutionary Negative Selection Algorithm. The function optimization experiments are conducted to demonstrate the performance of the Evolutionary Negative Selection Algorithm. And the experimental results show that with the negative selection operator, the Evolutionary Negative Selection Algorithm has better ability of escaping from local optimizations and getting a stable performance. At the same time, aiming at the function optimization problem, the empiristic methods of setting the self set size and the updating number of the self set at every generation are also given in this paper, both of which are important parameters about the negative selection operator, which will affect the performance of the algorithm very much.