TY - JFULL AU - M. F. Omar and R. A. Salam and R. Abdullah and N. A. Rashid PY - 2007/6/ TI - Multiple Sequence Alignment Using Optimization Algorithms T2 - International Journal of Computer and Information Engineering SP - 1511 EP - 1520 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/14036 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 5, 2007 N2 - Proteins or genes that have similar sequences are likely to perform the same function. One of the most widely used techniques for sequence comparison is sequence alignment. Sequence alignment allows mismatches and insertion/deletion, which represents biological mutations. Sequence alignment is usually performed only on two sequences. Multiple sequence alignment, is a natural extension of two-sequence alignment. In multiple sequence alignment, the emphasis is to find optimal alignment for a group of sequences. Several applicable techniques were observed in this research, from traditional method such as dynamic programming to the extend of widely used stochastic optimization method such as Genetic Algorithms (GAs) and Simulated Annealing. A framework with combination of Genetic Algorithm and Simulated Annealing is presented to solve Multiple Sequence Alignment problem. The Genetic Algorithm phase will try to find new region of solution while Simulated Annealing can be considered as an alignment improver for any near optimal solution produced by GAs. ER -