A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

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.

Authors

• Jing Tang Hebei University of Technology
• Yingke Chen Department of Computer and Information Sciences, Northumbria University, UK
• Zixuan Deng
• Yanping Xiang
• Colin Paul Joy

Keywords

• Heuristic Search and Game Playing: Evaluation and Analysis
• Multidisciplinary Topics and Applications: Autonomic Computing