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 t...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences Vol. 644; p. 119256
Main Authors: Yang, Yue, Ma, Yongjie, Wang, Minghao, Wang, Peidi
Format: Journal Article
Language:English
Published: Elsevier Inc 01.10.2023
Subjects:
Dynamic multi-objective optimization
Gene editing
Gene sequencing
Prediction
Support vector machine
Support vector machine
Dynamic multi-objective optimization
Gene editing
Prediction
Gene sequencing
ISSN:0020-0255
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. •Gene sequencing using support vector machine was designed for gene classification.•A prediction strategy that can use all chromosome information is designed.•A novel gene editing strategy was designed to achieve direct assign values to genes.
ISSN:0020-0255
DOI:10.1016/j.ins.2023.119256