RNA genetic algorithm based on octopus learning mechanism
Lifeng Zhang, Qiuxuan Wu, Xiaoni Chi, Jian Wang, Botao Zhang, Weijie Lin, Sergey A. Chepinskiy, Anton Zhilenkov, Yanbin Luo, Farong Gao · 2021
Genetic algorithms are often easy to fall into local optimum, Inspired by octopus RNA gene editing ability and learning ability, this paper proposed an RNA genetic algorithm based on octopus learning mechanism (LRNA-GA), which uses a single RNA chain to represent the individuals of the population, Imitating the octopus's A-to-G RNA editing method to replace traditional gene mutations, using behavioral learning to design the RNA chain, and determining the possibility of RNA editing by evaluating the RNA chain, so as to quickly jump out of the local optimal solution. The effectiveness of LRNA-GA is tested through typical benchmark functions, and it has fast search capabilities and high accuracy.