학술논문

A hybrid genetic algorithm with chemical reaction optimization for multiple sequence alignment
Document Type
Conference
Source
2019 22nd International Conference on Computer and Information Technology (ICCIT) Computer and Information Technology (ICCIT), 2019 22nd International Conference on. :1-6 Dec, 2019
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Simulation
Hidden Markov models
Genetics
Classification algorithms
Task analysis
Information technology
Optimization
Bioinformatics
Multiple Sequence Alignment
Genetic Algorithm
Chemical Reaction Optimization
Language
Abstract
Multiple Sequence alignment is the ultimate challenging tasks of biological science. It is used for comparison or difference or similarities in these sequences of data. Here, we applied a pragmatic Genetic Algorithm (GA) & Chemical Reaction Optimization (CRO) apparently the most suitable and familiar expansion technique and influenced by the natural genetic structure. Inquiring the magnificent alignment of a biological sequence set is classified as an NP-hard optimization problem for that, GA-CRO algorithms are capable to drive this complication. To find good results, we are going to show the benchmark dataset, the suggested approach is compared with those of the current tools like the SB-PIMA, SAGA, RBT-GA and GAPAM, HMMT. The simulation results recommend that our method be a viable solution with the other methods in terms of efficiency with the appropriate selection of parameters.