A hybrid genetic algorithm with chemical reaction optimization for multiple sequence alignment.

2019 ◽  
Author(s):  
Sajib Chatterjee ◽  
Promal barua ◽  
M. M. Hasibuzzaman ◽  
Afrin Iftiea ◽  
Tarpan Mukharjee ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Chemical Reaction ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Hybrid Genetic Algorithm ◽  
Chemical Reaction Optimization ◽  
Multiple Sequence ◽  
Reaction Optimization

Hybrid Genetics Algorithms for Multiple Sequence Alignment

2016 ◽  
pp. 346-366 ◽  
Author(s):  
John Tsiligaridis
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Traveling Salesman Problem ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Hybrid Genetic Algorithm ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Multiple Sequence ◽  
The Traveling Salesman Problem

The purpose of this chapter is to present a set of algorithms and their efficiency for the consistency based Multiple Sequence Alignment (MSA) problem. Based on the strength and adaptability of the Genetic Algorithm (GA) two approaches are developed depending on the MSA type. The first approach, for the non related sequences (no consistency), involves a Hybrid Genetic Algorithm (GA_TS) considering also Tabu Search (TS). The Traveling Salesman Problem (TSP) is also applied determining MSA orders. The second approach, for sequences with consistency, deals with a hybrid GA based on the Divide and Conquer principle (DCP) and it can save space. A consistent dot matrices (CDM) algorithm discovers consistency and creates MSA. The proposed GA (GA_TS_VS) also uses TS but it works with partitions. In conclusion, GAs are stochastic approaches that are proved very beneficial for MSA in terms of their performance.

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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