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Genetic and Evolutionary Computation Conference

GECCO 2003: Genetic and Evolutionary Computation — GECCO 2003 pp 483–494Cite as

Multi-objectivity as a Tool for Constructing Hierarchical Complexity

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2723))

Abstract

This paper presents a novel perspective to the use of multi-objective optimization and in particular evolutionary multi-objective optimization (EMO) as a measure of complexity. We show that the partial order feature that is being inherited in the Pareto concept exhibits characteristics which are suitable for studying and measuring the complexities of embodied organisms. We also show that multi-objectivity provides a suitable methodology for investigating complexity in artificially evolved creatures. Moreover, we present a first attempt at quantifying the morphological complexity of quadruped and hexapod robots as well as their locomotion behaviors.

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Author information

Authors and Affiliations

  1. Artificial Life and Adaptive Robotics (A.L.A.R.) Lab, School of Computer Science, University of New South Wales, Australian Defence Force Academy Campus, Canberra, Australia

    Jason Teo, Minh Ha Nguyen & Hussein A. Abbass

Authors
  1. Jason Teo
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  2. Minh Ha Nguyen
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  3. Hussein A. Abbass
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Teo, J., Nguyen, M.H., Abbass, H.A. (2003). Multi-objectivity as a Tool for Constructing Hierarchical Complexity. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_60

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  • DOI: https://doi.org/10.1007/3-540-45105-6_60

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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