Abstract
In this paper, we deal with an important issue generally omitted in the current literature on evolutionary multiobjective optimization: on-line adaptation. We propose a revised version of our micro-GA for multiobjective optimization which does not require any parameter fine-tuning. Furthermore, we introduce in this paper a dynamic selection scheme through which our algorithm decides which is the “best’ crossover operator to be used at any given time. Such a scheme has helped to improve the performance of the new version of the algorithm which is called the micro-GA2 (μGA2). The new approach is validated using several test function and metrics taken from the specialized literature and it is compared to the NSGA-II and PAES.
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Toscano Pulido, G., Coello Coello, C.A. (2003). The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization. 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_18
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DOI: https://doi.org/10.1007/3-540-36970-8_18
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