Abstract
As a novel and representative multi-task optimisation (MTO) paradigm, multi-factorial evolutionary algorithm (MFEA) can solve multiple self-contained tasks simultaneously. Its overall performance highly depends on control parameters. The aim of this research work is to analyse three parameters, namely, probability of individual learning, probability of intra-crossover and probability of inter-crossover, controlled by the user. Experimental results on MTO problems demonstrate the superiority of MFEA with a smaller probability of individual learning in a fair competitive environment. While the influence of probabilities of intra-crossover and inter-crossover is unpredictable based on the task's features, the basic selection principle and the optimal value are provided based on massive simulated data.
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