CLC number: TP301
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-08-06
Cited: 0
Clicked: 1296
Zhiyu DUAN, Shunkun YANG, Qi SHAO, Minghao YANG. PEGA: probabilistic environmental gradient-driven genetic algorithm considering epigenetic traits to balance global and local optimizations[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(6): 839-855.
@article{title="PEGA: probabilistic environmental gradient-driven genetic algorithm considering epigenetic traits to balance global and local optimizations",
author="Zhiyu DUAN, Shunkun YANG, Qi SHAO, Minghao YANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="6",
pages="839-855",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300170"
}
%0 Journal Article
%T PEGA: probabilistic environmental gradient-driven genetic algorithm considering epigenetic traits to balance global and local optimizations
%A Zhiyu DUAN
%A Shunkun YANG
%A Qi SHAO
%A Minghao YANG
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 6
%P 839-855
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300170
TY - JOUR
T1 - PEGA: probabilistic environmental gradient-driven genetic algorithm considering epigenetic traits to balance global and local optimizations
A1 - Zhiyu DUAN
A1 - Shunkun YANG
A1 - Qi SHAO
A1 - Minghao YANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 6
SP - 839
EP - 855
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300170
Abstract: epigenetics’ flexibility in terms of finer manipulation of genes renders unprecedented levels of refined and diverse evolutionary mechanisms possible. From the epigenetic perspective, the main limitations to improving the stability and accuracy of genetic algorithms are as follows: (1) the unchangeable nature of the external environment, which leads to excessive disorders in the changed phenotype after mutation and crossover; (2) the premature convergence due to the limited types of epigenetic operators. In this paper, a probabilistic environmental gradient-driven genetic algorithm (PEGA) considering epigenetic traits is proposed. To enhance the local convergence efficiency and acquire stable local search, a probabilistic environmental gradient (PEG) descent strategy together with a multi-dimensional heterogeneous exponential environmental vector tendentiously generates more offsprings along the gradient in the solution space. Moreover, to balance exploration and exploitation at different evolutionary stages, a variable nucleosome reorganization (VNR) operator is realized by dynamically adjusting the number of genes involved in mutation and crossover. Based on the above-mentioned operators, three epigenetic operators are further introduced to weaken the possible premature problem by enriching genetic diversity. The experimental results on the open Congress on Evolutionary Computation-2017 (CEC’ 17) benchmark over 10-, 30-, 50-, and 100-dimensional tests indicate that the proposed method outperforms 10 state-of-the-art evolutionary and swarm algorithms in terms of accuracy and stability on comprehensive performance. The ablation analysis demonstrates that for accuracy and stability, the fusion strategy of PEG and VNR are effective on 96.55% of the test functions and can improve the indicators by up to four orders of magnitude. Furthermore, the performance of PEGA on the real-world spacecraft trajectory optimization problem is the best in terms of quality of the solution.
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