Tracking extrema in dynamic environments
Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.
Unable to display preview. Download preview PDF.
- 1.Angeline, P. J. (1995). Adaptive and Self-Adaptive Evolutionary Computations. In Computational Intelligence: A Dynamic System Perspective, M. Palaniswami, Y. Attikiouzel, R. Marks, D. Fogel and T. Fukuda (eds.), Piscataway, NJ: IEEE Press, pp. 152–163.Google Scholar
- 2.Angeline, P. J., Fogel, D. B. and Fogel, L. J. (1996). A Comparison of Self-Adaptive Update Rules for Finite State Machines in Dynamic Environments. In Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, L. Fogel, P. Angeline and T. Bäck (eds.). Cambridge, MA: MIT Press, pp. 441–450.Google Scholar
- 3.Bäck, T. and Schwefel, H.-P. (1993). An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation, 1 (1), pp. 1–24.Google Scholar
- 4.Fogel, D.B. (1995). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Piscataway, NJ: IEEE Press.Google Scholar
- 5.Fogel, D.B., Fogel, L.J. and Atmar, J.W. (1991). Meta-Evolutionary Programming. Proc. of the 25th Asilomar Conference on Signals, Systems and Computers, R.R. Chen (ed.), San Jose, CA: Maple Press, pp. 540–545.Google Scholar
- 6.Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966). Artificial Intelligence through Simulated Evolution, New York: John Wiley & Sons.Google Scholar
- 7.Fogel, L.J., Angeline, P.J. and Fogel, D.B. (1995). An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines. In Evolutionary Programming IV: Proceedings of the Fourth Annual Conference on Evolutionary Programming, J. McDonnell, R. Reynolds, and D. Fogel (eds), Cambridge, MA: MIT Press, pp. 355–366.Google Scholar
- 9.Schwefel, H.-P. (1995). Evolution and Optimum Seeking. New York: John Wiley & Sons.Google Scholar
- 10.Yao, X. and Liu, Y. (1996). Fast Evolutionary Programming, In Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, L. Fogel, P. Angeline and T. Bäck (eds.). Cambridge, MA: MIT Press, pp. 451–460.Google Scholar