Abstract
Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time. We establish different benchmark scenarios where the capacity changes every \(\tau \) iterations according to a uniform or normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by \(\tau \) and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios when the frequency of changes is not too high.
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References
Eiben, A., Smith, J.: Introduction to Evolutionary Computing, 2nd edn. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-662-44874-8
Nguyen, T., Yao, X.: Continuous dynamic constrained optimization: the challenges. IEEE Trans. Evol. Comput. 16(6), 769–786 (2012)
Rakshit, P., Konar, A., Das, S.: Noisy evolutionary optimization algorithms - a comprehensive survey. Swarm Evol. Comput. 33, 18–45 (2017)
Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol. Comput. 6, 1–24 (2012)
Ameca-Alducin, M.-Y., Hasani-Shoreh, M., Neumann, F.: On the use of repair methods in differential evolution for dynamic constrained optimization. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 832–847. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77538-8_55
Pourhassan, M., Gao, W., Neumann, F.: Maintaining 2-approximations for the dynamic vertex cover problem using evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 903–910. ACM (2015)
Shi, F., Schirneck, M., Friedrich, T., Kötzing, T., Neumann, F.: Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1407–1414. ACM (2017)
Polyakovskiy, S., Bonyadi, M.R., Wagner, M., Michalewicz, Z., Neumann, F.: A comprehensive benchmark set and heuristics for the traveling thief problem. In: Proceedings of Conference on Genetic and Evolutionary Computation, pp. 477–484. ACM (2014)
Corder, G.W., Foreman, D.I.: Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. Wiley, Hoboken (2009)
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This work has been supported through Australian Research Council (ARC) grant DP160102401.
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Roostapour, V., Neumann, A., Neumann, F. (2018). On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_13
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DOI: https://doi.org/10.1007/978-3-319-99253-2_13
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