Abstract
This work introduces a general mathematical framework for non-stationary fitness functions which enables the exact definition of certain problem properties. The properties’ influence on the severity of the dynamics is analyzed and discussed. Various different classes of dynamic problems are identified based on the properties. Eventually, for an exemplary model search space and a (1, λ)-strategy, the interrelation of the offspring population size and the success rate is analyzed. Several algorithmic techniques for dynamic problems are compared for the different problem classes.
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References
Branke, J. (1999a). Evolutionary approaches to dynamic optimization problems: A survey. In J. Branke & T. Bäck (Eds.), Evolutionary algorithms for dynamic optimization problems (pp. 134–137). (part of GECCO Workshops, A. Wu (ed.))
Branke, J. (1999b). Memory enhanced evolutionary algorithms for changing optimization problems. In 1999 Congress on Evolutionary Computation (pp. 1875–1882). Piscataway, NJ: IEEE Service Center.
Cobb, H. G. (1990). An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments (Tech. Rep. No. 6760 (NLR Memorandum)). Washington, D.C.: Navy Center for Applied Research in Artificial Intelligence.
Cobb, H. G., & Grefenstette, J. J. (1993). Genetic algorithms for tracking changing environments. In S. Forrest (Ed.), Proc. of the Fifth Int. Conf. on Genetic Algorithms (pp. 523–530). San Mateo, CA: Morgan Kaufmann.
De Jong, K. A., & Spears, W. M. (1991). An analysis of the interacting roles of population size and crossover in genetic algorithms. In H.-P. Schwefel & R. Männer (Eds.), Parallel Problem Solving from Nature: 1st Workshop, PPSN I (pp. 38–47). Berlin: Springer.
Deb, K., & Agrawal, S. (1999). Understanding interactions among genetic algorithm parameters. In W. Banzhaf & C. Reeves (Eds.), Foundations of Genetic Algorithms 5 (pp. 265–286). San Francisco, CA: Morgan Kaufmann.
Goldberg, D. E., & Smith, R. E. (1987). Nonstationary function optimization using genetic algorithms with dominance and diploidy. In J. J. Grefenstette (Ed.), Proc. of the Second Int. Conf. on Genetic Algorithms (pp. 59–68). Hillsdale, NJ: Lawrence Erlbaum Associates.
Grefenstette, J. J. (1999). Evolvability in dynamic fitness landscapes: a genetic algorithm approach. In 1999 Congress on Evolutionary Computation (pp. 2031–2038). Piscataway, NJ: IEEE Service Center.
Lewis, J., Hart, E., & Ritchie, G. (1998). A comparison of dominance mechanisms and simple mutation on non-stationary problems. In A. E. Eiben, T. Bäck, M. Schoenauer, & H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature-PPSN V (pp. 139–148). Berlin: Springer. (Lecture Notes in Computer Science 1498)
Mahfoud, S. W. (1994). Population sizing for sharing methods (Tech. Rep. No. I11IGAL 94005). Urbana, IL: Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign.
Miller, B. L. (1997). Noise, sampling, and efficient genetic algorithms. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana, IL. (II1IGAL Report No. 97001)
Mori, N., Imanishi, S., Kita, H., & Nishikawa, Y. (1997). Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm. In T. Bäck (Ed.), Proc. of the Seventh Int. Conf. on Genetic Algorithms (pp. 299–306). San Francisco, CA: Morgan Kaufmann.
Morrison, R. W., & De Jong, K. A. (1999). A test problem generator for non-stationary environments. In 1999 Congress on Evolutionary Computation (pp. 2047–2053). Piscataway, NJ: IEEE Service Center.
Ronnewinkel, C., Wilke, C. O., & Martinetz, T. (2000). Genetic algorithms in time-dependent environments. In L. Kallel, B. Naudts, & A. Rogers (Eds.), Theoretical aspects of evolutionary computing (pp. 263–288). Berlin: Springer.
Rowe, J. E. (1999). Finding attractors for periodic fitness functions. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, & R. E. Smith (Eds.), Proc. of the Genetic and Evolutionary Computation Conf. GECCO-99 (pp. 557–563). San Francisco, CA: Morgan Kaufmann.
Smith, R. E. (1997). Population size. In T. Bäck, D. B. Fogel, & Z. Michalewicz (Eds.), Handbook of Evolutionary Computation (pp. El.1:1–5). Bristol, New York: Institute of Physics Publishing and Oxford University Press.
Trojanowski, K., & Michalewicz, Z. (1999). Searching for optima in non-stationary environments. In 1999 Congress on Evolutionary Computation (pp. 1843–1850). Piscataway, NJ: IEEE Service Center.
Weicker, K., & Weicker, N. (1999). On evolution strategy optimization in dynamic environments. In 1999 Congress on Evolutionary Computation (pp. 2039–2046). Piscataway, NJ: IEEE Service Center.
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Weicker, K. (2000). An Analysis of Dynamic Severity and Population Size. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_16
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DOI: https://doi.org/10.1007/3-540-45356-3_16
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