Optimization Research Based on Improved Genetic Algorithm

Abstract

Genetic algorithm itself has the characteristics of openness and parallel processing. Promoting its efficiency in optimizing recognition, widely applied in fields such as engineering, science, economics and computer science. However, the structural analysis time of traditional genetic algorithms is long and “premature” , the problem of low efficiency in designing and facing large-scale data still exists. In view of this, the article deeply analyzes the current shortcomings of genetic algorithms, and proposes suggestions to improve genetic algorithms with moderate functions, promote the integration of other optimization algorithms into genetic algorithms, and use big data technology to optimize genetic algorithms. Improvements are made to address the above issues, in order to provide useful references for optimizing genetic algorithms.