Abstract
In order to solve a Non-Stationary Optimization Problem (NSOP) it is necessary that the used algorithms have a set of suitable properties for being able to dynamically adapt the search to the changing fitness landscape. Our aim in this work is to improve our knowledge of existing canonical algorithms (steady-state, generational, and structured –cellular– genetic algorithms) in such a scenario. We study the behavior of these algorithms in a basic Dynamic Knapsack Problem, and utilize quantitative metrics for analyzing the results. In this work, we analyze the role of the mutation operator in the three algorithms and the impact of the frequency of dynamic changes in the resulting difficulty of the problem. Our conclusions outline that the steady-state GA is the best in fast adapting its search to a new problem definition, while the cellular GA is the best in preserving diversity to finally get accurate solutions. The generational GA is a tradeoff algorithm showing performances in between the other two.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alba, E. and Saucedo, J. (2005). Panmictic versus decentralized genetic algorithms for non-stationary problems. In Procs. the Sixth MIC. Electronic publication.
Alba, E. and Troya, J. M. (2000). Cellular evolutionary algorithms: Evaluating the influence of ratio. In Schoenauer, Marc, Deb, K., and et al., editors, Proc. of PPSN VI, volume 1917 of LNCS, pages 29–38, France. Springer.
Alba, E. and Troya, J. M. (2002). Improving flexibility and efficiency by adding parallelism to genetic algorithms. Statistics and Computing, 12(2):91–114.
Andrews, M. and Tuson, A. (2003). Diversity does not necessarily imply adaptability. In Barry, Alwyn M., editor, GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pages 118–122, Chigaco. AAAI.
Bäck, T., Fogel, D. B., and Michalewicz, Z., editors (1997). Handbook of Evolutionary Computation. Oxford University Press.
Branke, J. (2001). Evolutionary Optimization in Dynamic Environments. Klüwer Academic Publishers.
Dasgupta, D. and McGregor, D. R. (1992). Nonstationary function optimization using the structured genetic algorithm. In Männer, Reinhard and Manderick, B., editors, Proc. of PPSN II, pages 145–154, Amsterdan, Holand. Elsevier Science Publishers, B. V.
Ghosh, A., Tsutsui, S., and Tanaka, H. (1998). Function optimization in nonstationary environment using steady state genetic algorithms with aging of individuals. In Proc. of CEC’98, pages 666–671. IEEE Press.
Goldberg, D. E. and Smith, R. E. (1987). Nonstationary function optimization using genetic algorithms with dominance and diploidy. In Grefenstette, J. J., editor, Proc. of ICGA’87, pages 59–68. Lawrence Erlbaum Associates.
Lewis, J., Hart, E., and Ritchie, G. (1998). A comparison of dominance mechanisms and simple mutation on non-stationary problems. In Eiben, Agoston E., Bäck, T., and et al., editors, Proc. of PPSN V, volume 1498 of LNCS, pages 139–148, Berlin, Germany. Springer.
Manderick, B. and Spiessens, P. (1989). Fine-grained parallel genetic algorithms. In Schaffer, J. D., editor, Proc. of ICGA’89, pages 428–433, San Mateo, CA, USA. Morgan Kaufmann.
Mori, N., Kita, H., and Y, Nishikawa (1998). Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm. In Eiben, Agoston E., Bäck, T., and et al., editors, Proc. of PPSN V, volume 1498 of LNCS, pages 149–158, Berlin, Germany. Springer.
Morrison, R. W. (2003). Performance measurement in dynamic environments. In Barry, Alwyn M., editor, GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pages 99–102, Chicago. AAAI.
Ryan, C. (1997). Diploidy without dominance. In Alander, Jarmo T., editor, Proc. of the Third Nordic Workshop on Genetic Algorithms and their Applications, pages 63–70, Vaasa, Finnland. Department of Information Technology and Production Economics, University of Vaasa.
Salomon, R. and Eggenberger, P. (1998). Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Hao, J.-K., Lutton, Evelyne, and et al., editors, Proc. of the Third AE’98, volume 1363 of LNCS, pages 251–262, France. Springer.
Sarma, J. and De Jong, K. A. (1996). An analysis of the effect of the neighborhood size and shape on local selection algorithms. In Voigt, Hans-Michael, Ebeling, Werner, and et al., editors, Proc. of PPSN IV, volume 1141 of LNCS, pages 236–244, Berlin, Germany. Springer.
Sarma, J. and De Jong, K. A. (1999). The behavior of spatially distributed evolutionary algorithms in non-stationary environments. In Banzhaf, Wolfgang, Daida, J. M., Eiben, Agoston E., Garzon, Max H., Honavar, Vasant, Jakiela, Mark, and Smith, R. E., editors, Proc. of GECCO’99, pages 572–578, Orlando, FL, USA. Morgan Kaufmann.
Smith, J. E. and Vavak, F. (1999). Replacement strategies in steady state genetic algorithms: dynamic environments. Computing and Information Technology, 7(1):49–60.
Syswerda, G. (1991). A study of reproduction in generational and steady-state genetic algorithms. In Rawlins, Gregory J., editor, Proc. of FOGA’91, pages 94–101, San Mateo, CA, USA. Morgan Kaufmann.
Vavak, F. and Fogarty, T. C. (1996). Comparison of steady state and generational gas for use in nonstationary environments. In Proc. of CEC’96, pages 192–195. IEEE Press.
Weicker, K. (2000). An analysis of dynamic severity and population size. In Schoenauer, Marc, Deb, K., and et al., editors, Proc. of PPSN VI, volume 1917 of LNCS, pages 159–168, France. Springer.
Weicker, K. (2002). Performance measures for dynamic environments. In Merelo Guervós, Juan Julián, Adamidis, Panagiotis, Beyer, Hans-Georg, Fernández-Villacañas, José-Luis, and Schwefel, Hans-Paul, editors, Parallel Problem Solving from Nature – PPSN VII, pages 64–73, Berlin. Springer.
Whitley, D. (1989). The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In Schaffer, J. D., editor, Proc. of ICGA’89, pages 116–121, San Mateo, CA, USA. Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Alba, E., Saucedo Badia, J.F., Luque, G. (2007). A Study of Canonical GAs for NSOPs. In: Doerner, K.F., Gendreau, M., Greistorfer, P., Gutjahr, W., Hartl, R.F., Reimann, M. (eds) Metaheuristics. Operations Research/Computer Science Interfaces Series, vol 39. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-71921-4_13
Download citation
DOI: https://doi.org/10.1007/978-0-387-71921-4_13
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-71919-1
Online ISBN: 978-0-387-71921-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)