Abstract
This article investigates systematically the utility of performance measures in non-stationary environments. Three characteristics for describing the goals of a dynamic adaptation process are proposed: accuracy, stability, and recovery. This examination underpins the usage of the best fitness value as a basis for measuring the three characteristics in scenarios with moderate changes of the best fitness value. However, for dynamic problems without coordinate transformations all considered fitness based measures exhibit severe problems. In case of the recovery, a newly proposed window based performance measure is shown to be best as long as the accuracy level of the optimization is rather high.
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References
Angeline, P. J. (1997). Tracking extrema in dynamic environments. In P. J. Angeline, R. G. Reynolds, J. R. McDonnell, & R. Eberhart (Eds.), Evolutionary Programming VI (pp. 335–345). Berlin: Springer. (Lecture Notes in Computer Science 1213)
Branke, J. (1999). Evolutionary algorithms for dynamic optimization problems: A survey (Tech. Rep. No. 387). Karlsruhe, Germany: Institute AIFB, University of Karlsruhe.
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.
Collard, P., Escazut, C., & Gaspar, A. (1997). An evolutionary approach for time dependant optimization. International Journal on Artificial Intelligence Tools, 6 (4), 665–695.
Dasgupta, D., & McGregor, D. R. (1992). Nonstationary function optimization using the structured genetic algorithm. In R. Männer & B. Manderick (Eds.), Parallel Problem Solving from Nature 2 (Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, Brussels 1992) (pp. 145–154). Amsterdam: Elsevier.
De Jong, K. (2000). Evolving in a changing world. In Z. Ras & A. Skowron (Eds.), Foundation of intelligent systems (pp. 513–519). Berlin: Springer.
De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Unpublished doctoral dissertation, University of Michigan.
Feng, W., Brune, T., Chan, L., Chowdhury, M., Kuek, C. K., & Li, Y. (1997). Benchmarks for testing evolutionary algorithms (Tech. Rep. No. CSC-97006). Glasgow, UK: Center for System and Control, University of Glasgow.
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. (1992). Genetic algorithms for changing environments. In R. M:anner & B. Manderick (Eds.), Parallel Problem Solving from Nature 2 (Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, Brussels 1992) (pp. 137–144). Amsterdam: Elsevier.
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.
Hadad, B. S., & Eick, C. F. (1997). Supporting polyploidy in genetic algorithms using dominance vectors. In P. J. Angeline, R. G. Reynolds, J. R. McDonnell, & R. Eberhart (Eds.), Evolutionary Programming VI(pp. 223–234). Berlin: Springer. (Lecture Notes in Computer Science 1213)
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 Natur—PPSN V (pp. 139–148). Berlin: Springer. (Lecture Notes in Computer Science 1498)
Mori, N., Kita, H., & Nishikawa, Y. (1996). Adaptation to a changing environment by means of the thermodynamical genetic algorithm. In H. Voigt, W. Ebeling, & I. Rechenberg (Eds.), Parallel Problem Solving from Nature— PPSN IV (Berlin, 1996) (Lecture Notes in Computer Science 1141) (pp. 513–522). Berlin: Springer.
Mori, N., Kita, H., & Nishikawa, Y. (1998). Adaptation to a changing environment by means of the feedback thormodynamical geentic algorithm. In A. E. Eiben, T. Bäck, M. Schoenauer, & H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature— PPSN V (pp. 149–158). Berlin: Springer. (Lecture Notes in Computer Science 1498)
Salomon, R., & Eggenberger, P. (1997). Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In J.-K. Hao, E. Lutton, E. Ronald, M. Schoenauer, & D. Snyders (Eds.), Artificial Evolution: Third European Conf., AE’97 (pp. 251–262). Berlin: Springer.
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.
Vavak, F., Fogarty, T. C., & Jukes, K. (1996). A genetic algorithm with variable range of local search for adaptive control of the dynamic systems. In Proc. of the 2nd Int. Mendelian Conf. on Genetic Algorithms.?
Weicker, K. (2000). An analysis of dynamic severity and population size. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, & H.-P. Schwefel (Eds.), Parallel problem solving from nature— PPSN VI (pp. 159–168). Berlin: Springer.
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. (2002). Performance Measures for Dynamic Environments. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_7
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DOI: https://doi.org/10.1007/3-540-45712-7_7
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