二次演化建模在实时仿真中的应用

遗传程序设计在复杂系统的建模中表现出智能性，自适应性等特点，它可以找出能描述系统的静态和动态过程一系列相关联的函数或微分方程，为描述复杂系统提供了一种有效的手段，针对传统的遗传程序设计方法的搜索效率低，所建模型的精度不高等缺点，提出了一种新的演化建模算法：二次演化建模，该算法引入人工智能中的系统公告板来公布最好的树及其子树，从而加速了优化过程，使之达到实时仿真的目的，并将其应用于描述一个实时仿真系统－－轮机仿真系统，并结合仿真系统的特点来指导遗传算法，减少其搜索的盲目性，实验的结果表明无论在算法的求解速度还是模型的精度上二次演化建模算法均优于传统的遗传程序设计方法。

A NEW METHOD OF EVOLUTIONARY MODELING USED IN REAL-TIME SIMULATION

Genetic programming (GP) has a strong ability in model-building of complex systems, and can find a series of correlative function or differential equation to describe the static or dynamic process of simulation systems, so it is an effective means of describing complex systems. But GP has the following disadvantages: the efficiency is low and the precision of models isn't high, so a new evolutionary modeling algorithm (two stage evolutionary modeling, TSEM) has to be proposed. In the TSEM algorithm the system bulletin of the AI is introduced to proclaim some better solutions, so the genetic operating utilizes the bulletin information. The TSEM algorithm is used to a real-time system: marine engine system, and utilize the characteristic of a simulation system to guide the GP search and reduce the searching blindness. The experimental results show that the TSEM algorithm is better than the traditional GPs in speed and precision of models.