随机搜索算法 meaning in English
random searching rs
Examples
- Considering the one - sidedness and inaccuracy of knowledge discovery only from single - color database , an approach is proposed to discover knowledge from 1331 groups of mix - color database with partial least - square regression , based on measuring and learning 400 groups of single - color database . by this method , the mean error decreases when converting from rgb to cmyk , the precision of color matching is improved , and the automatic and general problem in color matching is further solved
本文基于统计学习理论构造了一种快速自适应随机搜索算法,证明了算法的收敛性.给出了一种简易实用的宽带天线匹配设计新方法.应用该自适应算法进行天线匹配设计,不仅算法简单,易于编程实现;而且能够快速设计出具有较好性能的匹配网络,非常适用于各种短波、超短波天线的匹配设计问题 - In this paper we study the key technology to the implementation of genetic algorithm and put forward a particular scheme of how to determine the parameters and operations when classify , including individual encoding , fitness function design , ga operators design , etc . thus , we give the method of how to mining classification rules using ga in theory
遗传算法是一种基于生物进化论和分子遗传学的全局随机搜索算法。本论文对应用遗传算法实现分类规则挖掘的关键技术进行了分析，包括个体编码、适应度函数的设计、遗传操作算子的设计等，从理论上阐述了基于遗传算法的分类规则挖掘的方法。 - Therefore , the original optimization model is transformed into the problem of cross sectional area optimization . this paper had great research on the development of optimization algorithm by analyzing typical optimal search method , such as greedy algorithm , simulated annealing algorithm , neural network and genetic algorithm ( ga ) . according to the characteristics of truss structure , we choose genetic algorithm as the solution way
本文在研究优化算法发展过程的基础上，分析了典型的优化搜索方法：确定性算法如贪婪算法，随机搜索算法如模拟退火算法，人工智能算法如神经网络及遗传算法，根据桁架结构优化的特点，最终选择以遗传算法作为桁架结构优化设计的主要算法。 - The reason why we integrate them is that k - means algorithm is a mountain climbing method , which is easy convergent to local extremum , and sensitive to the original condition , but its convergent speed is relatively fast , and that genetic algorithm is a random searching method , which can find the whole extremum in a rather big probability , and non - sensitive to the original condition , but its convergent speed relatively slow
之所以将：二者结合在一起，是回为k一均值算法是一种爬山法，容易收敛到冈部极小值，对初始条件较敏感，但收敛速度较快，而遗传算法是卞dl随机搜索算法，能够以较大概率找到全局最忧解，且对们始条件个敏感，但收敛速度较慢。