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Multi-objectivity as a Tool for Constructing Hierarchical Complexity
Jason Teo, Minh Ha Nguyen, and Hussein A. Abbass
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.
{j.teo,m.nguyen,h.abbass}@adfa.edu.au
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.
LNCS 2723, p. 483 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003