An overview of computational intelligence algorithms

Studies in Computational Intelligence

Volumn 437, Issue , 2013, Pages 63-94

  Ghosh, Sanchita ^a   Konar, Amit ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84867506518     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-30997-7_2     Document Type: Review

References (35)

1. Zimmermann, H.J.: Fuzzy Set Theory and Its Applications. Kluwer Academic, Dordrecht (1991)
2. Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, NY (1980)
3. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948 (1995)
4. Storn, R., Price, K.: Differential evolution-A Simple and Efficient Heuristic for Global continuous spaces. Journal of Global Optimization 11(4), 341-359 (1997) (Pubitemid 127502202)
5. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
6. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Academic Press (2001) ISBN 1-55860-595-9
7. Das, S., Konar, A., Chakraborty, U.K.: Two Improved Differential Evolution Schemes for Faster Global Search. In: ACM-SIGEVO Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2005), Washington DC (June 2005)
8. Wallace, A.: The Geographical Distribution of Animals (Two Volumes). Adamant Media Corporation, Boston (2005)
9. Darwin, C.: The Origin of Species. Gramercy, New York (1995)
10. Hanski, I., Gilpin, M.: Metapopulation Biology. Academic, New York (1997)
11. Wesche, T., Goertler, G., Hubert, W.: Modified habitat suitabilityindex model for brown trout in southeastern Wyoming. North Amer. J. Fisheries Manage. 7, 232-237 (1987)
12. Hastings, A., Higgins, K.: Persistence of transients in spatially structured models. Science 263, 1133-1136 (1994) (Pubitemid 24106993)
13. Muhlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm: I. Continuous parameter optimization. Evol. Comput. 1, 25-49 (1993)
14. Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford Univ. Press, Oxford (1996)
15. Parker, K., Melcher, K.: The modular aero-propulsion systems simulation (MAPSS) users' guide. NASA, Tech. Memo. 2004-212968 (2004)
16. Simon, D., Simon, D.L.: Kalman filter constraint switching for turbofan engine health estimation. Eur. J. Control 12, 331-343 (2006)
17. Simon, D.: Optimal State Estimation. Wiley, New York (2006)
18. Mushini, R., Simon, D.: On optimization of sensor selection for aircraft gas turbine engines. In: Proc. Int. Conf. Syst. Eng., Las Vegas, NV, pp. 9-14 (August 2005) (Pubitemid 46028515)
19. Chuan-Chong, C., Khee-Meng, K.: Principles and Techniques in Combinatorics. World Scientific, Singapore (1992)
20. Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
21. Dorigo, M., Gambardella, L., Middendorf, M., Stutzle, T.: Special section on 'ant colony optimization'. IEEE Trans. Evol. Comput. 6(4), 317-365 (2002)
22. Blum, C.: Ant colony optimization: Introduction and recent trends. Phys. Life Reviews 2, 353-373 (2005) (Pubitemid 41781478)
23. Onwubolu, G., Babu, B.: New Optimization Techniques in Engineering. Springer, Berlin (2004)
24. Price, K., Storn, R.: Differential evolution. Dr. Dobb's Journal 22, 18-20, 22, 24, 78 (1997)
25. Storn, R.: System design by constraint adaptation and differential evolution. IEEE Trans. Evol. Comput. 3, 22-34 (1999)
26. Michalewicz, Z.: Genetic Algorithms Data Structures Evolution Programs. Springer, New York (1992)
27. Rumelhart, D.E., Zipser, D.: Feature discovery by competitive learning. Cognitive Science 9, 75-112 (1985)
28. Sejnowski, T.J.: Strong covariance with nonlinearly interacting neurons. J. Math Biology 4, 303-321 (1977) (Pubitemid 8188217)
29. Takeuchi, A., Amari, S.I.: Formation of topographic maps and columnar microstructures. Biological Cybernetics 35, 63-74 (1979)
30. Yegnanarayana, B.: Artificial Neural Networks. Prentice-Hall of India, New Delhi (1988)
31. Baird, L.C., Moore, A.W.: Gradient descent for general reinforcement learning. In: Advances in Neural Information Processing Systems, vol. 11. The MIT Press (1999)
32. Bertsekas, D.P.: Dynamic Programming And Optimal Control, vol. 12. Athena Scientific, Belmont (1995b)
33. Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning 8, 229-256 (1992)
34. Kullback, S.: Information theory and statistics. John Wiley and Sons, NY (1959)
35. Meuleau, N., Dorigo, M.: Ant colony optimization and stochastic gradient descent. Artificial Life 8(2), 103-121 (2002)