Abstract
Prediction strategies in dynamic evolutionary optimization aim at estimating the moving optimum after a change of the fitness function. Considering the predicted optimum for re-initialization of the population, the evolution strategy is led into the direction of the next optimum. We propose a new way to control the influence of the prediction depending on its estimated uncertainty. In addition, we construct a new benchmark generator for dynamic optimization problems, Dynamic Sine Benchmark, tailored to prediction approaches. For prediction of the moving optimum and uncertainty estimation we apply a temporal convolutional network (TCN) with Monte Carlo dropout. In the experimental study, we compare our approach to known prediction and re-initialization strategies. The results show the advantage of the new re-initialization strategy and TCNs with uncertainty estimation for complex problems up to a certain dimensionality.
Keywords
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This research is funded by the German Research Foundation through the Research Training Group SCARE (DFG-GRK 1765/2).
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Meier, A., Kramer, O. (2019). Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_34
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