Improving prediction in evolutionary algorithms for dynamic environments

The addition of prediction mechanisms in Evolutionary Algorithms (EAs) applied to dynamic environments is essential in order to anticipate the changes in the landscape and maximize its adaptability. In previous work, a combination of a linear regression predictor and a Markov chain model was used to enable the EA to estimate when next change will occur and to predict the direction of the change. Knowing when and how the change will occur, the anticipation of the change was made introducing useful information before it happens. In this paper we introduce mechanisms to dynamically adjust the linear predictor in order to achieve higher adaptability and robustness. We also extend previous studies introducing nonlinear change periods in order to evaluate the predictor's accuracy.

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