A dynamic multi-objective evolutionary algorithm based on gene sequencing and gene editing

Evolutionary Algorithm is a mature global optimization method with high robustness and wide applicability. It can effectively address complex problems that are difficult to be solved by traditional optimization algorithms. In evolution, it is still uncertain which gene parent can produce offspring through competition and eventually become an excellent individual of the last generation population. Inspired by the fact that gene sequencing can discover biological gene defects, this paper proposes a dynamic multi-objective evolutionary algorithm based on gene sequencing and gene editing (GSGE). The gene sequencing algorithm is designed using support vector machine (SVM), and correlation coefficient, as the result of sequencing, has also been verified. Combining stratified sampling and multi-population strategy, a prediction strategy that can use all chromosome information is designed. The new gene editing strategy edits individual genes to optimize the Pareto optimal front (PF) distribution, for the first time different types of genes can match different optimization strategies, which greatly improves the distribution of the population without affecting the convergence. The comparison experiment between GSGE and the state of the art algorithms is performed on 25 test functions and the Pareto optimal set (PS) curves obtained by dimension reduction fully demonstrate the significant advantages of GSGE.

Automated summary

Evolutionary Algorithm is a mature global optimization method that effectively tackles complex problems. This paper proposes a dynamic multi-objective evolutionary algorithm based on gene sequencing and gene editing (GSGE) to optimize the Pareto optimal front (PF) distribution. The GSGE algorithm combines gene sequencing with support vector machine (SVM) design, stratified sampling, and multi-population strategy. Comparison experiments on 25 test functions demonstrate the significant advantages of GSGE.