Abstract
Motivated by an experimental problem involving the identification of effective drug combinations drawn from a non-static drug library, this paper examines evolutionary algorithm strategies for dealing with changes of variables. We consider four standard techniques from dynamic optimization, and propose one new technique. The results show that only little additional diversity needs to be introduced into the population when changing a small number of variables, while changing many variables or optimizing a rugged landscape requires often a restart of the optimization process.
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.: Experimentelle optimierung einer Zweiphasendüse. Bericht 35 des AEG-Forschungsinstitut Berlin zum Projekt MHD-Staustrahlrohr (1986)
Rechenberg, I.: Case studies in evolutionary experimentation and computation. Computer Methods in Applied Mechanics and Engineering 186(2-4), 125–140 (2000)
Knowles, J.: Closed-Loop Evolutionary Multiobjective Optimization. IEEE Computational Intelligence Magazine 4(3), 77–91 (2009)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2001)
Larsen, A.: The Dynamic Vehicle Routing Problem. PhD thesis, Technical University of Denmark (DTU), Denmark (2001)
Cobb, H.G., Grefenstette, J.J.: Genetic algorithms for tracking changing environments. In: Proc. of ICGA, pp. 523–530 (1993)
Goldberg, D., Smith, R.E.: Nonstationary function optimization using genetic dominance and diploidy. In: Proc. of ICGA, pp. 59–68 (1987)
Kauffman, S.: Adaptation on rugged fitness landscapes. In: Lectures in the Sciences of Complexity, pp. 527–618 (1989)
Mahfoud, S.W.: Niching methods for genetic algorithms. PhD thesis, University of Illinois at Urbana-Champaign, USA (1995)
Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proc. of IEEE CEC, pp. 1843–1850 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Allmendinger, R., Knowles, J. (2010). Evolutionary Optimization on Problems Subject to Changes of Variables. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-15871-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
Online ISBN: 978-3-642-15871-1
eBook Packages: Computer ScienceComputer Science (R0)