This paper presents a novel perspective to the use of multiobjective 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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Teo, J., Nguyen, M. H., & Abbass, H. A. (2003). Multi-objectivity as a tool for constructing hierarchical complexity. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2723, 483–494. https://doi.org/10.1007/3-540-45105-6_60
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