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Improving Distributed Neuroevolution Using Island Extinction and Repopulation

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Applications of Evolutionary Computation (EvoApplications 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12694))

Abstract

Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is the use of islands, which are also popular in improving the performance of distributed evolutionary algorithms. However, islands may experience stagnation, which prevents their convergence towards better solutions and can result in wasted computation. This work evaluates utilizing an island extinction and repopulation mechanism to avoid premature convergence using Evolutionary eXploration of Augmenting Memory Models (EXAMM), an asynchronous island based neuroevolution algorithm that progressively evolves recurrent neural networks (RNNs). In island extinction and repopulation, all members of the worst performing island are erased periodically and repopulated with mutated versions of the global best RNN. This island based strategy is additionally compared to NEAT’s (NeuroEvolution of Augmenting Topologies) speciation strategy. Experiments were performed using two different real-world time series datasets (coal-fired power plant and aviation flight data). With statistical significance, results show that in addition to being more scalable, this island extinction and repopulation strategy evolves better global best genomes than both EXAMM’s original island based strategy and NEAT’s speciation strategy. The extinction and repopulation strategy is easy to implement, and can be generically applied to other neuroevolution algorithms.

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Combustion Systems under Award Number #FE0031547 and by the Federal Aviation Administration and MITRE Corporation under the National General Aviation Flight Information Database (NGAFID) award.

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Notes

  1. 1.

    These data sets are made publicly available at EXAMM GitHub repository: https://github.com/travisdesell/exact/tree/master/datasets/ for reproduction of these results.

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Acknowledgements

Most of the computation of this research was done on the high performance computing clusters of Research Computing at Rochester Institute of Technology [29]. We would like to thank the Research Computing team for their assistance and the support they generously offered to ensure that the heavy computation this study required was available.

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Authors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, 14623, USA

    Zimeng Lyu, Joshua Karns, AbdElRahman ElSaid, Mohamed Mkaouer & Travis Desell

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  1. Zimeng Lyu
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  2. Joshua Karns
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  3. AbdElRahman ElSaid
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  4. Mohamed Mkaouer
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  5. Travis Desell
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Correspondence to Zimeng Lyu .

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Editors and Affiliations

  1. ETSIIT-CITIC, University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

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Lyu, Z., Karns, J., ElSaid, A., Mkaouer, M., Desell, T. (2021). Improving Distributed Neuroevolution Using Island Extinction and Repopulation. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_36

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  • DOI: https://doi.org/10.1007/978-3-030-72699-7_36

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