Self-Organizing Genetic Algorithm: A Survey

organization systems are an increasingly attractive dynamic processes without a central control, emerge global order from local interactions in a bottom up approach. The advantage of blending the concept of self-organization enhances the working efficiency of other techniques to find a solution of huge search problem. Genetic Algorithms (GA) is such a technique, inspired by the natural evolution process, used to solve difficult optimization problem of large space solution, for an example, multiple sequence alignment (MSA) problem in a bioinformatics research. Self-organization technique automates the selection of appropriate parameter values of GA during execution without the user's intervention. An attempt towards applying Self-organizing Genetic Algorithm (SOGA) on MSA requires a complete knowledge of the various parameters of SO and its relationships. This lead us to make a complete survey on inherent properties of SO and the method of blending GA in order to develop a self-organizing genetic algorithm (SOGA) for MSA. The aim of the research is to make use of the efficiency of GA without getting any input from the non- trained users to tune the parameters in order to achieve the expected result.

[1]  Mohammad Mehdi Ebadzadeh,et al.  A novel hybrid algorithm for creating self-organizing fuzzy neural networks , 2009, Neurocomputing.

[2]  D. Thierens.  Adaptive mutation rate control schemes in genetic algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  IF You Discover,et al.  THE BIOLOGICAL SCIENCE. , 1923, Science.

[4]  F. Corpet.  Multiple sequence alignment with hierarchical clustering. , 1988, Nucleic acids research.

[5]  Jens Stoye,et al.  DCA: an efficient implementation of the divide-and-conquer approach to simultaneous multiple sequence alignment , 1997, Comput. Appl. Biosci..

[6]  A. Dress,et al.  Multiple DNA and protein sequence alignment based on segment-to-segment comparison. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Lara C. Stache,et al.  Internet and Society: Social Theory in the Information Age , 2012, Rhetoric and Public Affairs.

[8]  KaleJyoti,et al.  Genetic Algorithms and Evolutionary Computation , 2010 .

[9]  Sanghamitra Bandyopadhyay,et al.  New Genetic Operators for Solving TSP: Application to Microarray Gene Ordering , 2005, PReMI.

[10]  Shengxiang Yang,et al.  A self-organizing random immigrants genetic algorithm for dynamic optimization problems , 2007, Genetic Programming and Evolvable Machines.

[11]  D. Higgins,et al.  SAGA: sequence alignment by genetic algorithm. , 1996, Nucleic acids research.

[12]  C. Notredame,et al.  Recent progress in multiple sequence alignment: a survey. , 2002, Pharmacogenomics.

[13]  S. Kuppuswami,et al.  iMAGA: INTRON MULTIPLE ALIGNMENT USING GENETIC ALGORITHM , 2010 .

[14]  Zhuang Jian,et al.  A novel and accelerated genetic algorithm , 2008 .

[15]  Francis Heylighen,et al.  The Science of Self-Organization and Adaptivity , 1999 .

[16]  Kusum Deep,et al.  A self-organizing migrating genetic algorithm for constrained optimization , 2008, Appl. Math. Comput..

[17]  A. Opstal.  Dynamic Patterns: The Self-Organization of Brain and Behavior , 1995 .

[18]  C. Gondro,et al.  A simple genetic algorithm for multiple sequence alignment. , 2007, Genetics and molecular research : GMR.

[19]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[20]  Ju-Jang Lee,et al.  A self-organizing genetic algorithm for multimodal function optimization , 2006, Artificial Life and Robotics.

[21]  D. Higgins,et al.  T-Coffee: A novel method for fast and accurate multiple sequence alignment. , 2000, Journal of molecular biology.

[22]  Toshio Fukuda,et al.  Virus-evolutionary genetic algorithm for a self-organizing manufacturing system , 1996 .

[23]  Thomas D. Seeley,et al.  When Is Self-Organization Used in Biological Systems? , 2002, The Biological Bulletin.

[24]  Tzung-Pei Hong,et al.  Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process , 2001, Applied Intelligence.

[25]  Donald H. Kraft,et al.  GENETIC ALGORITHMS AND THE MULTIPLE SEQUENCE ALIGNMENT PROBLEM IN BIOLOGY , 2000 .

[26]  Fernando G. Lobo,et al.  A parameter-less genetic algorithm , 1999, GECCO.

[27]  Thomas Bäck,et al.  Self-adaptive mutation rates in genetic algorithm for inverse design of cellular automata , 2008, GECCO '08.

[28]  Malrey Lee,et al.  Multiple Sequence Alignment using GA and NN , 2009 .

[29]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[30]  A Amouda.  Self organizing genetic algorithm for multiple sequence alignment , 2011 .

[31]  J. Thompson,et al.  CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. , 1994, Nucleic acids research.

[32]  V. Isaeva.  Self-organization in biological systems , 2012, Biology Bulletin.

[33]  Robert C. Edgar,et al.  MUSCLE: multiple sequence alignment with high accuracy and high throughput. , 2004, Nucleic acids research.

[34]  Sunan Wang,et al.  Self-organizing genetic algorithm based tuning of PID controllers , 2009, Inf. Sci..

[35]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .