Abstract
For any optimization algorithm tuning the parameters is necessary for effective and effcient optimization. We use a meta-level evolutionary algorithm for optimizing the effectiveness and effciency of a load-balancing evolutionary algorithm. We show that the generated parameters perform statistically better than a standard set of parameters and analyze the importance of selecting a good region on the Pareto Front for this type of optimization.
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Caswell, D.J., Lamont, G.B. (2003). Multiobjective Meta Level Optimization of a Load Balancing Evolutionary Algorithm. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_13
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DOI: https://doi.org/10.1007/3-540-36970-8_13
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