Abstract
Time-dependent optimization problems pose a new challenge to evolutionary algorithms, since they not only require a search for the optimum, but also a continuous tracking of the optimum over time. In this paper, we will will use concepts from the ”forking GA” (a multi-population evolutionary algorithm proposed to find multiple peaks in a multi-modal landscape) to enhance search in a dynamic landscape. The algorithm uses a number of smaller populations to track the most promising peaks over time, while a larger parent population is continuously searching for new peaks. We will show that this approach is indeed suitable for dynamic optimization problems by testing it on the recently proposed Moving Peaks Benchmark.
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
J. Branke. Evolutionary algorithms for dynamic optimization problems - a survey. Technical Report 387, Insitute AIFB, University of Karlsruhe, February 1999.
J. Branke. Memory enhanced evolutionary algorithms for changing optimization problems. In Congress on Evolutionary Computation CEC99, volume 3, pages 1875–1882. IEEE, 1999.
S. Tsutsui, Y. Fujimoto, and A. Ghosh. Forking genetic algorithms: GAs with search space division schemes. Evolutionary Computation, 5 (1): 61–80, 1997.
F. Oppacher and M. Wineberg. The shifting balance genetic algorithm: Improving the ga in a dynamic environment. In W. Banzhalf et al., editor, Genetic and Evolutionary Computation Conference, volume 1, pages 504–510. Morgan Kaufmann, 1999.
K. De Jong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor MI, 1975.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag London
About this paper
Cite this paper
Branke, J., Kaussler, T., Smidt, C., Schmeck, H. (2000). A Multi-population Approach to Dynamic Optimization Problems. In: Parmee, I.C. (eds) Evolutionary Design and Manufacture. Springer, London. https://doi.org/10.1007/978-1-4471-0519-0_24
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0519-0_24
Publisher Name: Springer, London
Print ISBN: 978-1-85233-300-3
Online ISBN: 978-1-4471-0519-0
eBook Packages: Springer Book Archive