Abstract
The Efficient Global Optimization (EGO) is capable of using limited function evaluation budget to find the global optimum. However, EGO is by design not built for parallelization, which is an important technique to speed up the costly computer codes. Some approaches have been developed to fix this issue. e.g. Constant Liar Strategy. In this article we propose an alternative way to obtain multiple points in the Efficient Global Optimization cycle, where a niching evolution strategy is combined into the classic EGO framework. The new approach is discussed and compared to other methods which aim at the same goal. The proposed approach is also experimented on the selected test functions.
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The authors gratefully acknowledge financial support by the Netherlands Organisation for Scientific Research (NWO) within the project PROMIMOOC.
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Wang, H., Bäck, T., Emmerich, M.T.M. (2018). Multi-point Efficient Global Optimization Using Niching Evolution Strategy. In: Tantar, AA., Tantar, E., Emmerich, M., Legrand, P., Alboaie, L., Luchian, H. (eds) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI. Advances in Intelligent Systems and Computing, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-69710-9_11
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DOI: https://doi.org/10.1007/978-3-319-69710-9_11
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