Global Function Optimization Based on Gene Expression Programming with Differential Evolution
基于差分进化基因表达式编程的全局函数优化

To improve the efficiency in function optimization via Gene Expression Programming(GEP),Differential Evolution(DE) was introduced into GEP. A novel algorithm called DEGEPO was proposed. The main work of this paper included (1) the gene in GEP was redesigned to adapt global function optimization; (2) novel mutation and crossover operations were applied; (3) a parameter optimization algorithm based on GEP with DE called DEGEPO was proposed and it was also analyzed; (4) experiments demonstrated the efficiency and effectiveness of DEGEPO. Compared with basic GEP, the precision of DEGEPO increased 2-4 orders of magnitude averagely.