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Selecting evolutionary algorithms for black box design optimization problems

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Abstract

An algorithm selection method for black box design optimization problems is reported. It uses a simple and natural principle to select an algorithm set from a set of algorithm candidates. A set of benchmark problems is given, and the performance of each algorithm in the set is recorded in a knowledge base. Given an unknown problem, the default algorithm is run. An algorithm–problem feature is proposed and used to map to the most similar benchmark problem. Then the best algorithm for solving the problem is used in the second run. This process iterates until n runs have been made. The best result out of n runs is returned as the solution. Experimental results reveal that the algorithm–problem feature is a good problem identifier. Results are also reported when (1) both the training and testing set are the set of benchmark problems; and (2) the training set is the set of benchmark problems but the testing set is a set of real-world benchmark problems. The method works well on both scenarios. It attains almost the same performance as the best algorithm, and has better performance compared with random selection. As the best algorithm cannot be known a priori, the results confirm that the algorithm selection mechanism is effective. The performance of the algorithm as a function of n and the case when n is equal to two is also studied.

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Acknowledgements

This study was funded by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 125313).

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Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China

    Shiu Yin Yuen & Yang Lou

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Xin Zhang

Authors
  1. Shiu Yin Yuen
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  2. Yang Lou
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  3. Xin Zhang
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Correspondence to Shiu Yin Yuen.

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Shiu Yin Yuen declares that he has no conflict of interest. Yang Lou declares that he has no conflict of interest. Xin Zhang declares that he has no conflict of interest.

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Appendix

Appendix

See Tables 8, 9, 10, 11, and 12.

Table 9 Mean values and standard deviations on CEC 2013 benchmarks for \(n=20\)
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Table 10 Mean values and standard deviations on CEC 2011 benchmarks for \(n=20\)
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Table 11 Mean values and standard deviations on CEC 2013 benchmarks for \(n=2\)
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Table 12 Mean values and standard deviations on CEC 2011 benchmarks for \(n=2\)
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Yuen, S.Y., Lou, Y. & Zhang, X. Selecting evolutionary algorithms for black box design optimization problems. Soft Comput 23, 6511–6531 (2019). https://doi.org/10.1007/s00500-018-3302-y

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