How Multi - Objectivity Can Characterize The Complexities of Artificial Creatures Technical Report : CS 08 / 03 Jason Teo

This paper proposes a novel perspective to the use of evolutionary multiobjective optimization (EMO) as a paradigm for the characterization of complexity. Our objective is not to introduce a new measure of complexity but rather providing a framework for comparing the complexity of an object across different complexity scales. EMO provides a practical (from artificial life practitioners’ perspective) yet mathematically-sound methodology that, by combining existing measures of complexity, enables complexity comparisons to be conducted. We show empirically that the partial order feature inherited in the Pareto concept exhibits characteristics which are suitable for comparing between the complexities of artificially evolved embodied organisms. Moreover, we present a first attempt at quantifying the morphological complexity of organisms as well as their behaviors.