Abstract
Dynamic Environments present many challenges for Evolutionary Computing. Frequency of change and amplitude of change, all have dramatic effects of how a system will behave. This in conjunction with poor search operators can lead to populations that can’t react to change quickly, as they have become converged in the search space. This study presents an overview of some methods to minimize the impact of change, and allow algorithms to better react to change in Dynamic Environments. Through the use of a bare bones tunable dynamic environment, it is shown how the approaches implemented can provide algorithms with faster responses to change. These approaches are also shown to do this without having to redesign the algorithms search operators, and maintaining the same computational effort.
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Acknowledgements
This research is based upon works supported by the Science Foundation Ireland under Grant No. 13/IA/1850.
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Fagan, D., O’Neill, M. (2017). Exploring Target Change Related Fitness Reduction in the Moving Point Dynamic Environment. In: Martín-Vide, C., Neruda, R., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2017. Lecture Notes in Computer Science(), vol 10687. Springer, Cham. https://doi.org/10.1007/978-3-319-71069-3_5
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