A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

Jing Tang, Yingke Chen, Zixuan Deng, Yanping Xiang, Colin Paul Joy

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3870-3876. https://doi.org/10.24963/ijcai.2018/538

Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionaryalgorithm 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 algorithmperformance. We propose a group-based MFEA that groups tasks of similar types and selectivelytransfers the genetic information only within the groups. We also develop a new selection criterionand an additional mating selection mechanism in order to strengthen the effectiveness andefficiency of the improved MFEA. We conduct the experiments in both the cross-domain and intra-domainproblems.
Keywords:
Multidisciplinary Topics and Applications: Autonomic Computing
Heuristic Search and Game Playing: Evaluation and Analysis