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.

PEGA：一种考虑表观遗传特征平衡全局和局部优化的概率环境梯度驱动遗传算法

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