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ABSTRACT

Developmental systems have inherent properties favourable for scaling. The possibility to generate very large scale structures combined with gene regulation opens for systems where the genome size do not reflect the size and complexity of the phenotype. Despite the presence of scalability in nature there is limited knowledge of what makes a developmental mapping scalable. As such, there is few artificial system that show true scaling. Scaling for any system, biological or artificial, is a question of resources. Toward an understanding of the challenges of scalability the issue of scaling is investigated in an aspect of resources within the developmental model itself. The resources are decompositioned into domains that can be scaled separately each may influence on the outcome of development. Knowledge of the domains influence on scaling provide insight in scaling limitation and what target problems that can be scaled. The resources are decompositioned into three domains; Phenotypic, Developmental and Computational (PDC). The domains are placed along three axes in a PDC-space. To illustrate the principles of scaling in a PDC-space an experimental approach is taken.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.m Miscellaneous

General Terms:
Design, Experimentation

Keywords:
cellular computation, development, evolvable hardware