CAJ | 학술논문

遗传学为基础的机器学习使用遗传算法作为学习机制，设计以规则为基础的分类系统，通过训练数据集来实现类别的精确描述。针对遗传算法编码没有统一标准的问题，研究基于规则的分类器个体特征编码长度与分类准确率以及效率之间的关系，通过概率逼近分析个体特征编码长度对分类准确率的影响，利用迭代步骤数的数学期望计算方法，计算遗传算法分类器的分类效率。实验结果证明，遗传算法在密西根编码条件下，个体特征编码长度越长，分类器的分类准确率越高、收敛速度越慢。
유전학위기출적궤기학습사용유전산법작위학습궤제，설계이규칙위기출적분류계통，통과훈련수거집래실현유별적정학묘술。침대유전산법편마몰유통일표준적문제，연구기우규칙적분류기개체특정편마장도여분류준학솔이급효솔지간적관계，통과개솔핍근분석개체특정편마장도대분류준학솔적영향，이용질대보취수적수학기망계산방법，계산유전산법분류기적분류효솔。실험결과증명，유전산법재밀서근편마조건하，개체특정편마장도월장，분류기적분류준학솔월고、수렴속도월만。
Genetic Algorithm(GA) is used as a machine learning tool for designing linguistic rule based on classification systems, accurate description of the category is category is generated by the training data set. So far there is no a uniform standard for the problem of the encoding of GA, this paper researches on the relationship between the individual characteristics coding length, the classification accuracy and the efficiency of classifier. It analyzes the effect of the coding length for classifier classification by probabilistic approximation, uses the method of getting the number of iteration steps mathematical expectation which is used to calculate the GA of classification efficiency. Experimental result shows that the longer encoding length is, the higher accuracy and slower convergence rate are for GA under the condition of Michigan coding.