Genetic Algorithm with Improved Mutation Operator for Multiple Sequence Alignment

2015 ◽  
pp. 515-523 ◽  
Author(s):  
Rohit Kumar Yadav ◽  
Haider Banka
Keyword(s):  
Genetic Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Mutation Operator ◽  
Multiple Sequence

scholarly journals SOGA: space oriented genetic algorithm for multiple sequence alignment

2018 ◽  
Vol 7 (4.5) ◽  
pp. 481
Author(s):  
Ruchi Gupta ◽  
Pankaj Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Iterative Process ◽  
Search Space ◽  
Optimization Method ◽  
Motif Finding ◽  
Mutation Operator ◽  
Multiple Sequence ◽  
Crossover And Mutation

Multiple sequence alignment is one of the recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although several applicable techniques were observed in this re- search, from traditional method such as dynamic programming to the extent of widely used stochastic optimization method such as Simu- lated Annealing and motif finding for solving this problem, their use is limited by the computing demands which are necessary for ex- ploring such a large and complex search space. This paper presents a new genetic algorithm, namely SOGA (Space Oriented Genetic Algorithm for Multiple Sequence Alignment), which has two new mechanisms: the first generates the population with randomly inserting the space between the selected sequences and the second applying new crossover and mutation operator, within an iterative process, to generate new and better solutions. This method is simple and fast. Its performance will further be tested on standard benchmark databas- es and will be compared with well-known algorithms. However, as its solutions clears that there is scope for further improvement. 

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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