CFP last date
22 April 2024
Reseach Article

Software Project Scheduling by AGA

by Dinesh Bhagwan Hanchate, Rajankumar S. Bichkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 21
Year of Publication: 2014
Authors: Dinesh Bhagwan Hanchate, Rajankumar S. Bichkar
10.5120/16917-7079

Dinesh Bhagwan Hanchate, Rajankumar S. Bichkar . Software Project Scheduling by AGA. International Journal of Computer Applications. 96, 21 ( June 2014), 21-40. DOI=10.5120/16917-7079

@article{ 10.5120/16917-7079,
author = { Dinesh Bhagwan Hanchate, Rajankumar S. Bichkar },
title = { Software Project Scheduling by AGA },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 21 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 21-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number21/16917-7079/ },
doi = { 10.5120/16917-7079 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:22:48.393365+05:30
%A Dinesh Bhagwan Hanchate
%A Rajankumar S. Bichkar
%T Software Project Scheduling by AGA
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 21
%P 21-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes general techniques for adapting operators in SGA for software project scheduling problem. The use of adaptive of crossover and mutation gives chance to control the diversity. Adaptive nature also tends to give convergence in the complex solution. Crossover and Mutation probability changes accordingly the change in the fitness values. High fitter is kept in the next pool. AGA(Adaptive genetic algorithm) converges to sub-optimal solution in fewer generation than SGA. In this paper, we consider skilled employees as an important resource to calculate the cost of the project along with some constrains of tasks. The paper gives a near-optimal estimated cost of project by using AGA. Our algorithm employs adaptive approaches for calculation of fitness of individuals, crossover rate and mutation rate. The paper also considers the aspects of head count, effort and duration calculated by COCOMO-II. 1999. These parameters are used to verify the fitness of each chromosome to get estimated cost by AGA closer to the cost estimated by COCOMO-II.

References
  1. D. B. Hanchate A. Thorat, R. H. Ambole. review on multimode resrouce constrained project scheduling problem". IJCSET, 2012.
  2. T. Back. Varying the probability of mutation in genetic algorithms. In Proc. Third Int. Con. Genetic Algorithms, 1989.
  3. Charles F Baer. "does mutation rate depend on itself". Genetics, pages 295–304, 2008.
  4. Barry W. Boehm. Software engineering economics, 1981.
  5. Tao Zhang Carl K. Chang, Mark. J. Christensen. Genetic algorithms for project management. annals of Software Engineering, 11:107139, 2001.
  6. Carl K. Chang, Chikuang Chao, Thinh T. Nguyen, and Mark J. Christensen. Software project management net: A new methodology on software management. In COMPSAC, pages 534–539, 1998.
  7. Charles Darwin. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. London, 1859.
  8. Kalyanmoy Deb. Genetic algorithm in search and optimization: The technique and applications. In Proc. of Int. Workshop on Soft Computing and Intelligent Systems, pages 58–87, 1997.
  9. Rajankumar S. Bichkar Dinesh Bhagwan Hanchate. sps by combination of crossover types and changeable mutation sga. IJCA, pages 41–66, 2014.
  10. Charlesworth B Charlesworth D Crow JF Drake, JW. "rates of spontaneous mutation". Genetics, 1998.
  11. A. E. Eiben, Z. Michalewicz, M. Schoenauer, and J. E. Smith. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2007.
  12. CarenMarzban AntonelloPasini Ellen Haupt, ValliappaLakshmanan and John K. Williams. Environmental science models and artificial intelligence. Springer Science. , 2009.
  13. CarenMarzban AntonelloPasini Ellen Haupt, ValliappaLakshmanan and John K. Williams. Environmental science models and artificial intelligence. Springer Science. , 2009.
  14. J. Franciso Chicano Enrique Alba. sofware project management with gas". Information Sciences,SceinceDirect, 2007.
  15. Davis E. W. and G. E. Heidorn. ian algorithm for optimal project scheduling under multiple resource constraints. Management Science, pages 41–66, 1971.
  16. T C. Fogarty. Varying the probability of mutation in genetic algorithms. In Proc. Third Int. Con. Genetic Algorithms, 1989.
  17. Futuyam. The genetical theory of natural selection. 2009.
  18. J. J. Grefenstette. Optimization of control parameters for genetic algorithms,. IEEE Trans. Systems, Man, and Cybernetics, 16:122–128, 1986.
  19. Ren-Wang Li Gui Yang, Yujun Lu. Adaptive genetic algorithms for the job-shop scheduling problems in hanintelligent control and automation. In WCICA, 2008.
  20. T Haynes. A comparison of random search versus genetic programming as engines for collective adaptation. In In: Proc. of the ACM Symposium on Applied Computing, 1997.
  21. Jurgen Hesser and Reinhard Manner. towards an optimal mutation probability for genetic algorithms.
  22. E. Horowitz and S. Sahani. ,Fundamentals of Computer Algorithms. Galgotia Publications, 1999.
  23. Sam Hsiung and James Matthews. Introduction to Genetic Algorithms.
  24. Shengxiang Yang Imtiaz Korejo and ChangheLi. A comparative study of adaptive mutation operators for genetic algorithms. In MIC 2009: The VIII Metaheuristics International Conference, 2009.
  25. Kenneth Alan De Jong¡c. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan Ann Arbor, MI, USA, 1975.
  26. Bryant A. Julstrom. What have you done for me lately? adapting operator probabilities in a steady-state genetic algorithm. In ICGA, pages 81–87, 1995.
  27. PD Keightley. rates and fitness consequeces of new mutations in humans. Genetics, pages 295–304, 2012.
  28. Kosorukoff. Using incremental evaluation and adaptive choice of operators in a genetic algorithm,. In Proceedings of the Genetic and Evolutionary Computation Conference, 2002.
  29. Lam Law and K. Y. Szeto. Adaptive genetic algorithm with mutation and crossover matrices. In IJCAI, 2007.
  30. Keqin Li. scheduling parallel tasks on multiprocessor computers with efficient power management. IEEE, pages 41–66, 2010.
  31. Park Mark Anclif. "mutation rate threshold under chaning environments with sharp peak fitness function". Journal of the Korean Physical Society, 2008.
  32. Jeff D. Hamann Matthew P. Thompson and John Sessions. "selection and penalty strategies for genetic algorithms designed to solve spatial forest planning problems". International Journal of Forestry Researc. , page 14, 2009.
  33. Michalewich and D. Fogel. How to solve it ?: Modern heuristics. Springer, 2002.
  34. Z Michalewich. Genetic Algorithms + Data Structures = Evolution Programs. Springer,, 1996.
  35. S. Forest Mitchell and J. H. Holland. The royal road for genetic algorithms: Fitness landscapes and ga performance. In First ECAL,, 1992.
  36. S. Forest Mitchell and J. H. Holland. The royal road for genetic algorithms: Fitness landscapes and ga performance. In First ECAL, 1992. , 1992.
  37. Tom M. Mitchell. Machine Learning. McGraw-Hill Companies, Inc. , 1997.
  38. Himanshu Bhalchandra Dave Parag Himanshu Dave. Design and Analysis of Algorithms. Pearson Education, 2008.
  39. Y. Attikiouzel R. J. Marks II D. B. Fogel Peter J. Angeline, M. Palaniswami and T. Fukuda (eds. ). Adaptive and self-adaptive evolutionary computations. In Computational Intelligence: A Dynamic Systems Perspective, pages 152–163. IEEE Press, 1995.
  40. Michael Pinedo. Scheduling:Theory, Algorithms, and Systems. Kluwer Academic Publishers, 2013.
  41. and M. Kurttila. Pukkala. examining the performance of six heuristic optimisation techniques in different forest planning problems,. Silva Fennica, (2), 2005.
  42. SAN Ye Ren. Ziwu. improved adaptive genetic algorithm and its application research in parameter identification[j]. . Journal of System Simulation. , pages 41–66, 2006.
  43. S. Sariel S. Uyar and G. Eryigit. A gene based adaptive mutation strategy for genetic algorithms. In Proc. of the 2004 Genetic and Evolutionary Computation Conference, 2004.
  44. A. Preece S. Yang, F. Coenen and A. Macintosh (eds. ). Adaptive mutation using statistics mechansim for genetic algorithms,. In Research and Development in Intellignet Systems XX.
  45. J. Sarma and K. De Jong. Selection: generation gap methods. In Handbook on Evolutionary Computation, pages C2. 7:1– C2. 7:5. Institute of Physics Publishing and Oxford University Press, Bristol and New York, 1997.
  46. H. P. Schwefel. Numerical Optimization of Computer Models. Wiley, Chichester, 1981.
  47. J. E. Smith and T. C. Fogarty. Operator and parameter adaptation in genetic algorithms. Soft Computing, 1(2):81–87, 1997.
  48. R. S. Sutton and A. G. Barto. , Reinforcement Learning An Introduction. MIT Press, 1998.
  49. D. Thierens. Adaptive mutation using statistics mechansim for genetic algorithms,. In Research and Development in Intellignet Systems XX.
  50. D. Thierens. Selection schemes,elitist recombination and selection intensity. In in International conference of genetic algorithm.
  51. D. Thierens and D. E. Goldberg. Mixing in genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms, pages 38–45, 1993.
  52. Ayse Daloglu Vedat Togan. Adaptive approaches in genetic algorithms to catch the global optimisation. In Seventh international congress advancess in civil engineering, turkey, Oct- 2006. , 2006.
  53. Henry David Venema, Paul H. Calamai, and Paul W. Fieguth. Forest structure optimization using evolutionary programming and landscape ecology metrics. European Journal of Operational Research, 164(2):423–439, 2005.
  54. Michele McDonough Venkatraman. Types of task relationships in microsoft project. 2011.
  55. Jing Xiao, Xian-Ting Ao, and Yong Tang. Solving software project scheduling problems with ant colony optimization. Computers & OR, 40(1):33–46, 2013.
  56. Zhang and Jeffrey J. P. Tsai. machine learning and software engineering". In Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

AGA COCOMO-II Software Cost Estimation Project Scheduling.