A Multigroup Parallel Genetic Algorithm for Multiple Sequence Alignment

2011 ◽  
pp. 308-316 ◽  
Author(s):  
Jiawei Luo ◽  
Li Zhang ◽  
Cheng Liang
Keyword(s):  
Genetic Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Parallel Genetic Algorithm ◽  
Multiple Sequence

Ant Colony with Genetic Algorithm Based on Planar Graph for Multiple Sequence Alignment

2010 ◽  
Vol 9 (2) ◽  
pp. 274-281 ◽  
Author(s):  
Xuyu Xiang ◽  
Dafan Zhang ◽  
Jiaohua Qin ◽  
Yuanyuan Fu
Keyword(s):  
Genetic Algorithm ◽  
Planar Graph ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Ant Colony ◽  
Multiple Sequence

A QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON PROBABILISTIC CODING FOR MULTIPLE SEQUENCE ALIGNMENT

2010 ◽  
Vol 08 (01) ◽  
pp. 59-75 ◽  
Author(s):  
HONG-WEI HUO ◽  
VOJISLAV STOJKOVIC ◽  
QIAO-LUAN XIE
Keyword(s):  
Genetic Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Quantum Algorithms ◽  
Quantum Memory ◽  
Genetic Operators ◽  
Multiple Sequence ◽  
Memory Register ◽  
Probabilistic Coding ◽  
Quantum Parallelism

Quantum parallelism arises from the ability of a quantum memory register to exist in a superposition of base states. Since the number of possible base states is 2n, where n is the number of qubits in the quantum memory register, one operation on a quantum computer performs what an exponential number of operations on a classical computer performs. The power of quantum algorithms comes from taking advantages of quantum parallelism. Quantum algorithms are exponentially faster than classical algorithms. Genetic optimization algorithms are stochastic search algorithms which are used to search large, nonlinear spaces where expert knowledge is lacking or difficult to encode. QGMALIGN — a probabilistic coding based quantum-inspired genetic algorithm for multiple sequence alignment is presented. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The experimental results show that QGMALIGN can compete with the popular methods, such as CLUSTALX and SAGA, and performs well on the presenting biological data. Moreover, the addition of genetic operators to the quantum-inspired algorithm lowers the cost of overall running time.

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