Abstract
It is the aim of every country to have a good and strong defence system for the protection of its people and its assets. In this paper we have shown the application of Genetic Algorithms (GA’S) for optimizing the expected survival value of an asset subjected to air attacks. We have developed a mathematical model of the problem subjected to relevant constraints. We have solved this problem with the help of Binary Coded Genetic Algorithm or Simple Genetic Algorithm (SGA) and Real Coded Genetic Algorithm (RCGA). For RCGA we have developed a new crossover operator called the Quadratic Crossover Operator (QCX), which is multi parental in nature. This operator makes use of three parents to produce an offspring, which lies at the point of extrema of the quadratic curve passing through the three selected parents. The working of the operator is shown with help of a simple, steady state Genetic Algorithm having conditional elitism. After testing the validity of this algorithm on several test problems we applied it to the mathematical model of the air defence problem. The comparison of results show that although both the techniques are well suited for solving the above said problem, RCGA with QCX operator gives slightly better results then the SGA.
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
Schwefel, H.P.: Evolution and Optimum Seeking. John Wiley and Sons Inc., Chichester (1995)
Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Fogel, D.B.: Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (1995)
Bach, T.F., Hoffmeister, Schwefel, H.P.: A Survey Of Evolutionary Strategies. In: Belew, R.K., Booker (eds.) Proceedings of the 4th International Conference on Genetic Algorithms, pp. 2–9. Morgan Kaufman, San Francisco (1991)
Davis, L.: Adapting Operator Probabilities In Genetic Algorithms. In: Schaffer, J.D. (ed.) Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 61–69. Morgan Kaufman, San Francisco (1989)
Radcliffe, N.J.: Forma Analysis and Random Respectful Recombination. In: Proc. 7th ICGA, pp. 246–253 (1997)
Wright, A.: Genetic Algorithms for Real Parameter Optimization. FOGA, 31–36 (1991)
Janikow, C.Z., Michalewicz, Z.: An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms. In: Proc., 4th ICGA, pp. 31–36 (1991)
Michalewicz, Z.: Genetic algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, New York (1996)
Eshelman, L., Schaffer, J.D.: Real coded Genetic algorithms and Interval Schemata. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms, vol. 2, pp. 187–202. Morgan Kaufman, Los Altos (1993)
Voigt, H.M., Muhlenbein, H., Gvetkovic, D.: Fuzzy Recombination for Breeder Genetic Algorithm. In: Proc. 6th ICGA, pp. 104–111 (1995)
Ono, I., Kobayashi: A Real Coded Genetic Algorithm for Function Optimization using Unimodal Distribution Crossover. In: Proceedings of 7th International Conference on Genetic Algorithms, pp. 246–253 (1997)
Kita, H., Ono, I., Kobayashi, S.: Multiparental Extension of the Unimodal Normal Distribution Crossover for Real Coded Genetic Algorithms. In: Proceedings of 1999 congress on evolutionary computation, pp. 1581–1587 (1999)
Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling Real Coded Genetic Algorithms. Operators and Tools for Behavioral Analysis, Artificial intelligence review 12(4), 265–319 (1998)
De Jong, K.: An Analysis Of The Behavior of a Class Of Genetic Adaptive Systems, Doctoral dissertation, University of Michigan, Dissertation Abstracts International, 36 (10), 5140B, University Microfilms No. 76-9381 (1975)
Storn, R., Price, K.: Differential Evolution a Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. J. Global Optimization 11, 341–359 (1997)
Levy, A.V., Montalvo, A., Gomez, S., Caderon, A.: Topics in Global Optimization. In: Dold, A., Eckman, B. (eds.) Numerical Analysis, Proceedings, Cocyoo, Mexico. Lecture Notes in Mathematics, vol. 909, pp. 18–33. Springer, Heidelberg (1981)
Bramin, F.H.: Widely Convergent Method for Finding Multiple Solutions of Simultaneous Non Linear Equations, I.B.M.J. R and D, New York (1972)
More, J.J., Garbow, B.S., Hillstorm, K.E.: Testing Unconstrained Optimization Software. ACM Transactions on Mathematical Software 7(1), 17–41 (1981)
Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)
Wood, K.: Deterministic Network Interdiction. Mathematical and Computer Modelling 17, 1–18 (1993)
Garcia, M.: The Design and Evaluation of Physical Protection Systems. Butterworth- Heinemann, Boston (2001)
GAMS-CPLEX. EX (2004), http://www.gams.com/solvers/solvers.htm#CPLEX
Brown, G., Carlyle, M., Diehl, D., Kline, J., Wood, K.: How to Optimize Theater Ballistic Missile Defense. Operations Research 53(5) (2005)
Brown, G., Carlyle, M., Harney, R., Skroch, E., Wood, K.: Interdicting a Nuclear Weapons Project in Review (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pant, M., Deep, K. (2006). Building a Better Air Defence System Using Genetic Algorithms. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_114
Download citation
DOI: https://doi.org/10.1007/11892960_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
eBook Packages: Computer ScienceComputer Science (R0)