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Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology

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Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10200))

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Abstract

This paper reports on IMDEA (In-Memory database Dynamic Evolutionary Algorithm), an approach to dynamic evolutionary optimization exploiting in-memory database (IMDB) technology to expedite the search process subject to change events arising at runtime. The implemented system benefits from optimization knowledge persisted on an IMDB serving as associative memory to better guide the optimizer through changing environments. For this, specific strategies for knowledge processing, extraction and injection are developed and evaluated. Moreover, prediction methods are embedded and empirical studies outline to which extent these methods are able to anticipate forthcoming dynamic change events by evaluating historical records of previous changes and other optimization knowledge managed by the IMDB.

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Acknowledgments

The work for this paper was generously supported by the HPI Future SOC Lab in the scope of the project “Big Data in Bio-inspired Optimization”.

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Authors and Affiliations

  1. Hochschule Karlsruhe, University of Applied Sciences, Karlsruhe, Germany

    Julia Jordan & Bernd Scheuermann

  2. CAS Software AG, Karlsruhe, Germany

    Julia Jordan

  3. SAP Innovation Center Network, Potsdam, Germany

    Wei Cheng

Authors
  1. Julia Jordan
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  2. Wei Cheng
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  3. Bernd Scheuermann
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Corresponding author

Correspondence to Bernd Scheuermann .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Jordan, J., Cheng, W., Scheuermann, B. (2017). Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10200. Springer, Cham. https://doi.org/10.1007/978-3-319-55792-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-55792-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55791-5

  • Online ISBN: 978-3-319-55792-2

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