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A survey of evolutionary algorithms using metameric representations

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Abstract

Evolutionary algorithms have been used to solve a number of variable-length problems, many of which share a common representation. A set of design variables is repeatedly defined, giving the genome a segmented structure. Each segment encodes a portion, frequently a single component, of the solution. For example, in a wind farm design problem each segment may encode the position and height of a single turbine. This is described as a metameric representation, with each segment referred to as a metavariable. The number of metavariables can vary among solutions, requiring modifications to the traditional fixed-length evolutionary operators. This paper surveys the literature that uses metameric representations with a focus on the problems being solved, the specifics of the representation, and the modifications to evolutionary operators. While there is little cross-referencing among the cited articles, it is demonstrated that there is already a strong overlap in their methodologies. By considering problems using a metameric representation as a single class, greater recognition of commonalities and differences among these works can be achieved. This could allow for the development of more efficient metameric evolutionary algorithms.

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Figure adapted from Ahrari et al. [41]

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Figure adapted from Ryerkerk et al. [3]

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Figure adapted from Ryerkerk et al. [3]

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Figure adapted from Ryerkerk et al. [3]

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Notes

  1. An adaptation of this work is included in the doctoral dissertation submitted by the primary author to Michigan State University [2].

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Acknowledgements

We thank the anonymous reviewers for their time and constructive comments which helped improve the quality of this paper. 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.

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  1. Michigan State University, East Lansing, MI, 48824, 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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Ryerkerk, M., Averill, R., Deb, K. et al. A survey of evolutionary algorithms using metameric representations. Genet Program Evolvable Mach 20, 441–478 (2019). https://doi.org/10.1007/s10710-019-09356-2

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