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A novel selection mechanism for evolutionary algorithms with metameric variable-length representations

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Abstract

Metameric problems are variable-length optimization problems whose representations take on an at least partially segmented structure. This is referred to as a metameric representation. Frequently, each of these segments defines one of a number of analogous components in the solution. Examples include the nodes in a coverage network or turbines in a wind farm. Locating optimal solutions requires, in part, determining the optimal number of components. Evolutionary algorithms can be applied but require modifications to the traditional fixed-length operators. This study proposes a new selection operator for metameric problems: length niching selection. First, the population is partitioned into several niches based on solution length. A window function determines at which lengths a niche is formed. Local selection is then applied within each niche independently, resulting in a new parent population formed by a diverse set of solution lengths. A coverage and a wind farm problem are used to demonstrate the effectiveness of the new operator.

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Notes

  1. This work is adapted from the doctoral dissertation submitted by the primary author to Michigan State University (Ryerkerk 2018).

  2. Source code is available at: https://github.com/ryerkerk/metameric.

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Acknowledgements

This material is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI-0939454 to BEACON Center for the Study of Evolution in Action. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA). The views, opinions, and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. This work was supported in part by Michigan State University through computational resources provided by the Institute for Cyber-Enabled Research.

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  1. Michigan State University, East Lansing, USA

    Matt Ryerkerk, Ron Averill, Kalyanmoy Deb & Erik Goodman

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  1. Matt Ryerkerk
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  2. Ron Averill
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  3. Kalyanmoy Deb
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  4. Erik Goodman
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Correspondence to Matt Ryerkerk.

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Communicated by V. Loia.

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Ryerkerk, M., Averill, R., Deb, K. et al. A novel selection mechanism for evolutionary algorithms with metameric variable-length representations. Soft Comput 24, 16439–16452 (2020). https://doi.org/10.1007/s00500-020-04953-1

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  • DOI: https://doi.org/10.1007/s00500-020-04953-1

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