摘要
人工植物算法是最近几年提出来的一种新颖的智能优化算法,把一个植物的生长过程映射为一个智能优化问题。它为那些高维多模问题提出了一种新的解决方法,但是,当把人工植物算法应用到现实问题中时,有时会遇到适应值很耗时的计算,如优化目标和随机问题中存在随机因素的不确定规划问题,或适应值需要通过很多复杂计算才能近似计算等问题。所以,在人工植物算法中需要采取一些预测适应值的策略,采取了基于代进化控制的结合神经网络预测模型的策略(GAPOA)。
Artificial plant optimization algorithm(APOA) is a recent proposed evolutionary computation in which the growing process of one tree is mapped into the optimized problem.It is suitable to solve the problem of high dimensional multi-modal optimization.One main difficulty in applying APOA to a real-world application is that APOA usually need a large number of fitness evaluations before a satisfying result can be obtained.This paper introduces a new "training GRNN using APOA"(GAPOA) that does not evaluate all new branch positions owning a fitness by the real fitness function and associated reliability fitness of each branch of the plant.This paper undertakes generalized regression neural network(GRNN) to approximate fitness in Artificial Plants Optimization Algorithm.
出处
《电脑开发与应用》
2013年第7期18-20,共3页
Computer Development & Applications