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http://dx.doi.org/10.3837/tiis.2022.01.005

A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection  

Lan, Yang (School of Computer Science and Technology, Taiyuan University of Science and Technology)
Xie, Lijie (School of Computer Science and Technology, Taiyuan University of Science and Technology)
Cai, Xingjuan (School of Computer Science and Technology, Taiyuan University of Science and Technology)
Wang, Lifang (School of Computer Science and Technology, Taiyuan University of Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.1, 2022 , pp. 80-96 More about this Journal
Abstract
Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.
Keywords
integrated strategy; intelligent medicine; many-objective evolutionary algorithm; skin cancer;
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