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Content Provider | IET Digital Library |
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Author | Xu, Qingzheng Zhang, Jianhang Fei, Rong Li, Wei |
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. |
Starting Page | 620 |
Ending Page | 625 |
Page Count | 6 |
Volume Number | 2020 |
e-ISSN | 20513305 |
Issue Number | Issue 13, Jul (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2020/13 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1230 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2020-01-13 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Control Parameter Data Analysis Data Handling Technique Evolutionary Computation Individual Learning Knowledge Engineering Technique Learning in AI Massive Simulated Data MFEA MTO Problem Multifactorial Evolutionary Algorithm Multiple Self-contained Task Optimisation Technique Probability Smaller Probability Statistics |
Content Type | Text |
Resource Type | Article |
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