Comparison of a Fuzzy EP Algorithm and an AIS in Dynamic Optimization Tasks

In this paper we compare a specific evolutionary programming algorithm with a basic artificial immune system-based method in a dynamic combinatorial optimization task. Evolutionary algorithms are known to produce competitive results in optimization tasks, where only a single best solution is desirable. Artificial immune systems, however, can simultaneously find many different competitive solutions, and this property makes them an interesting choice in dynamic optimization environments. The performance of these two algorithms is compared using a nonparametric statistical framework that does not require any knowledge regarding the output distribution of the algorithms

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