Abstract
The task of adjusting the values of the various parameters in the systems, especially the evolutionary systems, leads to a lot of sub-optimality. Besides the ideal parameter values may be different in different contexts and different stages of the algorithm. This necessitates the construction of adaptive systems that can tune their parameters themselves, leading to close to ideal performance. This chapter explores the various types of adaptive systems. The chapter first presents the types of adaptive systems based on their dynamism. Here we discuss the static, deterministic, adaptive and self-adaptive systems. We then discuss the level of adaptation in these systems. Here we present the environment, individual, population and component level adaptation. We present numerous examples of adaptive systems of various kinds.
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References
Angeline, P.J.: Adaptive and self-adaptive evolutionary computation. In: Palaniswami, M., Attikiouzel, Y., Marks, R.J., Fogel, D., Fukuda, T. (eds.) Computational Intelligence: A Dynamic System Perspective, pp. 152–161. IEEE Press, New York (1995)
Angeline, P.J.: Two self-adaptive crossover operators for genetic programming. In: Angeline, P.J., Kinnear, K.E. (eds.) Advances in Genetic Programming, vol. 2, pp. 89–109. MIT Press, Cambridge (1996)
Angeline, P.J., Saunders, G.M., Pollack, J.B.: An Evolutionary Algorithm that Constructs Recurrent Neural Networks. IEEE Trans. Neural Netw. 5(1) (1994)
Arabas, J., Michalewicz, Z., Mulawka, J.: GAVaPS—A genetic algorithm with varying population size. In: Proc. 2nd IEEE Conf. Evol. Comput., pp. 73–78 (1994)
Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford (1996)
Bäck, T., Hoffmeister, F.: Extended selection mechanisms in genetic algorithms. In: Belew, R.K., Booker, L.B. (eds.) Proc. of the Fourth Intl’ Conf. Genetic Algorithms. Morgan Kaufmann, San Francisco (1991)
Baker, J.E.: Adaptive selection methods for genetic algorithms. In: Grefenstette, J.J. (ed.) Proc. of the First Intl. Conf. on Genetic Algorithms and their Appl., Erlbaum, Mahwah (1985)
Chakraborty, U.K., Dastidar, D.G.: Using reliability analysis to estimate the number of generations to convergence in genetic algorithm. Inf. Process. Lett. 46, 199–209 (1993)
Cho, S.B., Shimohara, K.: Evolutionary Learning of Modular Neural Networks with Genetic Programming. Applied Intelligence 9, 191–200 (1998)
Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: Schaffer, J.D. (ed.) Proc. of the Third Intl. Conf. on Genetic Algorithms. Morgan Kaufmann, San Francisco (1989)
Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Los Alamitos (1995)
Funabiki, N., Kitamichi, J., Nishikawa, S.: An Evolutionary Neural Network Approach for Module Orientation Problems. IEEE Trans. Syst. Man Cybern. – Part B Cybern. 28(6), 849–855 (1998)
Goldberg, D.E.: Sizing populations for serial and parallel genetic algorithms. In: Schaffer, J.D. (ed.) Proc. of the Third Intl. Conf. on Genetic Algorithms. Morgan Kaufmann, San Francisco (1989)
Hinterding, R., Michalewicz, Z., Eiben, A.E.: Adaptation in Evolutionary Computation: A Survey. In: Proc. of the Fourth IEEE Conf. on Evol. Comput., pp. 65–69 (1997)
Ho, Y., Pepyne, D.: Simple explanation of the no-free-lunch theorem and its implications. J. Opt. Theory Appl. 155, 549–570 (2002)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Lobo, F.G., Lima, C.F., Michalewicz, Z.: Parameter Setting in Evolutionary Algorithms. Springer, Heidelberg (2007)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1992)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1999)
Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann–Holzboog Verlag, Stuttgart (1973)
Schwefel, H.P.: Evolution and Optimum Seeking. Wiley, Chichester (1995)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for search. Santa Fe Institute, Tech. Rep. SFI-TR-95-02-010 (1995), http://citeseer.nj.nec.com/wolpert95no.html
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
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Shukla, A., Tiwari, R., Kala, R. (2010). Adaptive Systems. In: Towards Hybrid and Adaptive Computing. Studies in Computational Intelligence, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14344-1_19
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DOI: https://doi.org/10.1007/978-3-642-14344-1_19
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