Abstract
Aiming at the optimization problem of shortest time limit - resource leveling, the paper first introduces Evolutionary Programming (EP) to solve it, and a new modified method based on evolutionary programming is proposed: the mutation operator of EP is improved by using the theory of Simulated An-nealing (SA), and without using repair operator. Then use Genetic Algorithm (GA), EP and the modified EP to solve this problem, the experimental results indicate that EP can optimize this problem effectively, and EP has better opti-mization performance than GA. The average evolution generation decreased significantly in the modified EP to approach the optimal solution, the variance after optimization decreases 42.64%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mitsuo, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. Tsinghua University Press, Beijing (2004)
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through simulated evolution. Wiley, New York (1966)
Ambati, B.K., Ambati, J., Mokhtar, M.M.: Heuristic combinatorial optimization by simulated Darwinian evolution: a polynomial time algorithm for the traveling salesman problem. Biological Cybemetics (1991)
Fogel, D.B.: System identification through simulated evolution: a machine learning approach to modeling. Ginn, Neeham Heights (1991)
Fogel, D.B., Fogel, L.J., Atmar, J.W.: Meta-evolutionary programming. In: Proceedings of 25th Asilomar Conference on Signals, Systems and Computers (1991)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Yib, P., Pao, Y.-H.: Combinatorial optimization with use of guided evolutionary simu-lated annealing. IEEE Trans. on Neural Networks (1995)
Li, X., Tan, W., Kang, L.S.: Research on balanced resource optimization based on genetic algorithm. Computer Engineering and Design, Beijing (2008)
Zhou, K., Jun, T.X., Xu, J.: A mathematical model and genetic algorithm for re-source optimization. J. Huazhong Univ. of Sci. & Tech. (Nature Science Edition), Wuhan (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luo, Y., Tang, J., Xu, S., Zhu, L., Li, X. (2012). Application and Research of Shortest Time Limit-Resource Leveling Optimization Problem Based on a New Modified Evolutionary Programming. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-34289-9_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34288-2
Online ISBN: 978-3-642-34289-9
eBook Packages: Computer ScienceComputer Science (R0)