CAJ | 학술논문

基因表达式编程（GEP）算法是遗传计算家族中的新成员，它参考生物进化的基因遗传规律而提出。笔者先后介绍了GEP 的应用背景和基本理论，重点研究了在改善种群多样性问题的优化策略，设计了将 GEP 与小生境技术结合的关键技术和算法流程，用定量方法确定个体之间的差异情况并动态调整适应度值，使得算法能够有效解决早熟收敛难题。实验部分用复杂函数建模验证了改进方法在进化速度和模型拟合度等方面的优势，为现实工作提供了有价值的预测模型和指导数据。
기인표체식편정（GEP）산법시유전계산가족중적신성원，타삼고생물진화적기인유전규률이제출。필자선후개소료GEP 적응용배경화기본이론，중점연구료재개선충군다양성문제적우화책략，설계료장 GEP 여소생경기술결합적관건기술화산법류정，용정량방법학정개체지간적차이정황병동태조정괄응도치，사득산법능구유효해결조숙수렴난제。실험부분용복잡함수건모험증료개진방법재진화속도화모형의합도등방면적우세，위현실공작제공료유개치적예측모형화지도수거。
Gene expression programming is a new member of genetic computing family and it is proposed by referring to gene inheritance rules. This paper introduces the application background and basis theories of GEP and lays its emphasis on the optimization strategy of improving population diversity. It designs the critical technology and algorithm flow for the combination of GEP and niche technique which can solve the difficulty in premature convergence through determining the individual difference and further adjusting the fitness value dynamically. In the experiment field,the new method,through complicated function modeling,is proved to be advantageous in evolutionary rate and model fitting,thus provide valuable predicting model and guidance data for practical work.