关键词: 多序列比对, 隐马尔可夫模型, 遗传算法, 对数似然概率

Abstract: Traditional method for training HMMs is Baum-Welch algorithm which is noted for the rapid convergence.However，this method can only lead to local optimization and effects quantity of sequence alignment.In response to these problems for training HMMs using Baum-Welch algorithm and integration of laws in biological genetics and evolution，a new HMMs training method based on BW-GA that combines traditional method and genetic algorithm is proposed.According to the need of genetic alignment and structure of HMMs，three genetic operations and coding methods are designed.HMMs is trained with 19 nuclear 5sRNA sequences by applying BW-GA，which makes the sequences generated have a higher log-likelihood of probability than just applying traditional methods，and a better multi-sequence alignment is generated.

Key words: multi-sequence alignment, hidden markov models, genetic algorithm, log likelihood probability