Optimize Velocity of Gene Function Prediction by Using Greedy-Genetic Algorithm on Computational Sequence Variants

The point which makes the Genetic Algorithm so robust in many kinds of complex situations is its flexibility to generate many populations by employing the crossover and mutation techniques. This is shown in solving a difficult problem like the computational DNA sequence variant which can be easily solved by applying a simple Genetic Algorithm. However, achieving a fast and optimized algorithm to generate any order of reproduction is a matter of time and cost. The main contribution of this paper is on the optimization in the selection stage of the Genetic Algorithm (GA) that proposes to build a new hybrid of the Greedy and the Genetic Algorithms for the computation of DNA sequences variant. This is achieved by integrate Greedy and Genetic algorithm, aim to pick up the most suitable parents on selection segment. Sequentially, fitness function combined with greedy algorithm is applied to reduce the number of node within the network and optimize the velocity of selection segment through genetic algorithm. In comparison with other well-known algorithm, this method optimized the complexity into O(nlogn).

[1]  Tracy Camp,et al.  Changing Trends in Modeling Mobility , 2012, J. Electr. Comput. Eng..

[2]  Sebastien Rey-Coyrehourcq,et al.  Half a Billion Simulations: Evolutionary Algorithms and Distributed Computing for Calibrating the Simpoplocal Geographical Model , 2015, ArXiv.

[3]  Frank Neumann,et al.  Time Complexity Analysis of Evolutionary Algorithms on Random Satisfiable k-CNF Formulas , 2016, Algorithmica.

[4]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[5]  Jehoshua Bruck,et al.  Neural network computation with DNA strand displacement cascades , 2011, Nature.

[6]  Yanshan Shi.  Comparing K-Nearest Neighbors and Potential Energy Method in classification problem. A case study using KNN applet by E.M. Mirkes and real life benchmark data sets , 2012, ArXiv.

[7]  Kara Dolinski,et al.  The BioGRID interaction database: 2015 update , 2014, Nucleic Acids Res..

[8]  Khurana Namita,et al.  Genetic Algorithm: A Search of Complex Spaces , 2011 .

[9]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[10]  Zhichao Miao,et al.  Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score , 2015, Nucleic acids research.

[11]  Pietro Simone Oliveto,et al.  On the runtime analysis of the Simple Genetic Algorithm , 2014, Theor. Comput. Sci..

[12]  Chang Wook Ahn,et al.  Elitism-based compact genetic algorithms , 2003, IEEE Trans. Evol. Comput..

[13]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[14]  Gurpreet Singh Bhamra,et al.  Ant colony algorithms in MANETs: A review , 2012, J. Netw. Comput. Appl..