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The Boon of Gene-Culture Interaction for Effective Evolutionary Multitasking

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Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

Multifactorial optimization (MFO) is a recently proposed paradigm for evolutionary multitasking that is inspired by the possibility of harnessing underlying synergies between outwardly unrelated optimization problems through the process of implicit genetic transfer. In contrast to traditional single-objective and multi-objective optimization, which consider only a single problem in one optimization run, MFO aims at solving multiple optimization problems simultaneously. Through comprehensive empirical study, MFO has demonstrated notable performance on a variety of complex optimization problems. In this paper, we take a step towards better understanding the means by which MFO leads to the observed performance improvement. In particular, since (a) genetic and (b) cultural transmission across generations form the crux of the proposed evolutionary multitasking engine, we focus on how their interaction (i.e., gene-culture interaction) affects the overall efficacy of this novel paradigm.

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Acknowledgement

This work was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme.

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Authors and Affiliations

  1. Computational Intelligence Lab, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Bingshui Da & Abhishek Gupta

  2. Rolls-Royce@NTU Corporate Lab C/o, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Yew Soon Ong

  3. College of Computer Science, Chongqing University, Chongqing, China

    Liang Feng

Authors
  1. Bingshui Da
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  2. Abhishek Gupta
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  3. Yew Soon Ong
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  4. Liang Feng
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Corresponding author

Correspondence to Yew Soon Ong .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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© 2016 Springer International Publishing Switzerland

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Da, B., Gupta, A., Ong, Y.S., Feng, L. (2016). The Boon of Gene-Culture Interaction for Effective Evolutionary Multitasking. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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