Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionary algorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently. Its efficiency is derived by implicitly transferring the genetic information among tasks. However, MFEA doesn't distinguish the information quality in the transfer compromising the algorithm performance. We propose a group-based M-FEA that groups tasks of similar types and selectively transfers the genetic information only within the groups. We also develop a new selection criterion and an additional mating selection mechanism in order to strengthen the effectiveness and efficiency of the improved MFEA. We conduct the experiments in both the cross-domain and intra-domain problems.

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