Abstract
This paper proposes an improvement of evolutionary algorithms for dynamic objective functions with a prediction mechanism based on the Autoregressive Integrated Moving Average (ARIMA) model. It extends the Infeasibility Driven Evolutionary Algorithm (IDEA) that maintains a population of feasible and infeasible solutions in order to react on changing objectives faster. Combining IDEA with ARIMA leads to a more efficient evolutionary algorithm that reacts faster to the changing objectives which profits from using information coming from the prediction mechanism and remains one time instant ahead of the original algorithm. Preliminary experiments performed on popular benchmark problems confirm that the IDEA-ARIMA outperforms the original IDEA algorithm in many cases.
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Filipiak, P., Michalak, K., Lipinski, P. (2011). Infeasibility Driven Evolutionary Algorithm with ARIMA-Based Prediction Mechanism. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_41
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DOI: https://doi.org/10.1007/978-3-642-23878-9_41
Publisher Name: Springer, Berlin, Heidelberg
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