Multi-objectivity as a tool for constructing hierarchical complexity

Authors:
Jason Teo;Minh Ha Nguyen;Hussein A. Abbass
Affiliations:
Artificial Life and Adaptive Robotics Lab, School of Computer Science, University of New South Wales, Canberra, Australia;Artificial Life and Adaptive Robotics Lab, School of Computer Science, University of New South Wales, Canberra, Australia;Artificial Life and Adaptive Robotics Lab, School of Computer Science, University of New South Wales, Canberra, Australia
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Year:
2003

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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.