Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Zi-Jia WANG, Jun-Rong JIAN, Zhi Hui ZHAN, Yun LI, Sam KWONG, Jun ZHANG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

19 Citations (Scopus)


Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this article introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in the candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.

Original languageEnglish
Pages (from-to)964-979
Number of pages16
JournalIEEE Transactions on Evolutionary Computation
Issue number4
Early online date23 Jun 2022
Publication statusPublished - Aug 2023
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2102102; in part by the National Natural Science Foundations of China (NSFC) under Grant 62106055, Grant 62176094, and Grant 61873097; in part by the Guangdong Natural Science Foundation under Grant 2022A1515011825, Grant 2020B010166002, Grant 2018B030312003, and Grant 2021B1515120078; in part by the National Research Foundation of Korea under Grant NRF-2021H1D3A2A01082705; and in part by the Hong Kong GRF-RGC General Research Fund under Grant 11209819 (CityU 9042816) and Grant 11203820 (CityU 9042598).

Publisher Copyright:
© 1997-2012 IEEE.


  • Differential evolution (DE)
  • evolutionary computation
  • gene targeting (GT)
  • large-scale optimization
  • simple and efficient


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