CAJ | 학술논문

　　基因表达式编程（GEP）算法是一种具有强大函数发现能力的新型进化算法。GEP在函数发现时如何确定合适的数值常量对算法的性能具有很大影响。提出了一种基于协同进化基因表达式编程的函数发现算法（GEP-DE），该算法的最大改进在于一种新的常量优化方法：在每一代中将函数发现的过程分为两个阶段：第一阶段，由标准GEP算法结合固定常量集确定函数结构；第二阶段，使用差分进化算法（DE）对第一阶段得出的函数结构的常量进行优化。实验结果表明， GEP-DE算法比重要文献中的常量处理方法其效果有较大提升，并且算法的综合性能也优于最新重要文献提出的GEP算法。
　　기인표체식편정（GEP）산법시일충구유강대함수발현능력적신형진화산법。GEP재함수발현시여하학정합괄적수치상량대산법적성능구유흔대영향。제출료일충기우협동진화기인표체식편정적함수발현산법（GEP-DE），해산법적최대개진재우일충신적상량우화방법：재매일대중장함수발현적과정분위량개계단：제일계단，유표준GEP산법결합고정상량집학정함수결구；제이계단，사용차분진화산법（DE）대제일계단득출적함수결구적상량진행우화。실험결과표명， GEP-DE산법비중요문헌중적상량처리방법기효과유교대제승，병차산법적종합성능야우우최신중요문헌제출적GEP산법。
Gene Expression Programming(GEP)is a powerful evolutionary algorithm widely used in function mining. and how to determine numeric constants has important influence to the performance of GEP. A novel approach of optimizing numeric con-stants based on co-evolutionary Gene Expression Programming(GEP-DE)is proposed in this paper. The main improvement in GEP-DE is to give a novel numeric constants optimization method, where the evolutionary process is divided into 2 phases in each generation:in the first phase, GEP focuses on optimizing the structure of function expression, and in the second one, DE focuses on optimizing the constant parameters. The experimental result on function mining problems shows that the performance of GEP-DE is better than that of the state-of-the-art GEP variants.