Effects of Different Implementations of a Real Random Number Generator on the Search Behavior of Multiobjective Evolutionary Algorithms

We often notice that different experimental results of the same algorithm were reported in the literature. Usually this is because computational experiments were performed under different settings. However, even when almost the same settings are used, similar results are not always obtained. Especially when we use our own implementation, it is very difficult to obtain similar experimental results to the reported ones in the literature. Of course, due to the stochastic nature of an evolutionary algorithm, its experimental results can be different in each run. However, its average results over many runs should be similar. Unfortunately, this is not always the case. We often encounter the situation where our own experimental results are clearly different from the reported ones in the literature. In this paper, we report our investigation results on the following question: why totally different results of MOEA/D were often reported in the literature. More specifically, we show that totally different results can be obtained when we generate random real numbers in a slightly different manner such as the normalization of integers in [0, 253 – 1] by dividing them by 253 or (253 – 1).