Adoption of Parallel Genetic Algorithms for the Solution of System of Equations

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Author(s):
Shilpa S Babalad, Anand M Shivapuji
Published Date:
February 29, 2012
Issue:
Volume 2, Issue 2
Page(s):
1 - 5
DOI:
10.7815/ijorcs.22.2012.015
Views:
4004
Downloads:
419

Keywords:
evolution, genetic algorithms, parallel programming, system of equations
Citation:
Shilpa S Babalad, Anand M Shivapuji, "Adoption of Parallel Genetic Algorithms for the Solution of System of Equations". International Journal of Research in Computer Science, 2 (2): pp. 1-5, February 2012. doi:10.7815/ijorcs.22.2012.015 Other Formats

Abstract

Analysis and understanding of physical systems require modeling the system as set of simultaneous linear/non linear equations and generating solutions to satisfy the system of equations. Analytical approach towards solving the system of equations remains practical so long as considerable constraints are imposed on the modeled system to bring in significant simplicity so as to retain the system model within the scope of defined algorithms for solving system of equations. The current work adopts the concept of genetic algorithm towards evolving a solution to a system of equations. The fundamental strength of genetic algorithms lies in the fact the solution generation is practically unconstrained permitting the methodology to become the superset for all possible realizable problems. One of the oft repeated and highlighted / drawbacks of genetic algorithm i.e. requirements of huge initial population and corresponding extended number of computations and hence time is addressed by exploiting multi-processing capabilities of the current generation hardware as well as system software. Adopted strategy for selecting the best population, implementation flow chart along with a case study is presented.

  1. Wang D and Zhiming Z, “Differential equations with symbolic computation”, Birkhauser Basel, 2005. doi:10.1007/3-7643-7429-2
  2. Versteeg H and Malalasekara W, “An introduction to computational fluid dynamics – The finite volume method”, Pearson, 2008.
  3. Smith I M and Griffiths D V, “Programming the finite element method”, John Wiley & Sons, 2004
  4. Hayt W H, Kemmerly J E and Durbin S M, “Engineering Circuits Analysis”, Tata McGraw-Hill Education, 2006
  5. Chapra S and Canale R, “Numerical Methods for Engineers”, McGraw-Hill Higher Education, 2009
  6. Hamming R W, “Numerical methods for scientists and engineers”, Dover Publications, 1973
  7. Sarkar T, Siarkiewicz K and Stratton R, “Survey of numerical methods for solution of large systems of linear equations for electromagnetic field problems”, IEEE Transactions on Antennas and Propagation, Vol, 29, No 6, 1981, pp: 847-856. doi:10.1109/TAP.1981.1142695
  8. Gerardo Canfora, Massimiliano Di Penta, Raffaele Esposito, Maria Luisa Villani (2005) “An approach for QoS-aware service composition based on genetic algorithms”. Proceedings of the 2005 conference on Genetic and evolutionary computation GECCO 05 (2005).
  9. Markus OLSCHOWKA, “A new pivoting strategy for Gaussian elimination”, Linear Algebra and its Applications, Vol 240, 1996, pp:131-151
  10. GNU (2011), “GCC, the GNU Compiler Collection, Version 4.6.1”, Available: http://gcc.gnu.org/gcc-4.6/
  11. OpenMP (2011), “The OpenMP API Specification for Parallel Programming, Version 3.1”, Available: http://openmp.org/wp/

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