A biologically inspired DNA-based cellular approach to developing complex adaptive systems

The development of most engineered systems usually involves large amounts of investment, yet their value may decrease significantly when operation environments change. The out-of-date systems are often discarded because upgrading usually costs more than replacing. While it has been recognized that system adaptability is required by both economical and mission-critical needs, there has been little research that explores and develops theories and methodologies for designing adaptive systems partly due to the traditional "static design" framing of engineering design problems.;A general way to solve design problems is to "Divide and Conquer" the problem whereby the design is decomposed through its various hierarchical layers in the hopes that it becomes simpler to address and realize at the lower levels. The systems which the D&C approach is suited to be applied to are those systems with a limited level of complexity, i.e. statically connected system elements. Adaptability requires flexibility which calls for allowing system elements to have dynamic connections. Doing so results in an increase of system complexity because more information is required to define it. As a result, the D&C based approach may find its limitations when being applied to such complex systems because one may not even know where to begin dividing. In order to cope with the complexity problem of adaptive systems design and avoid potential limitations of the D&C type top-down approach, we explore a more bottom-up approach to design. The basic idea of this approach is two-fold. First, to allow a system to exhibit adaptability the system must be able to self-organize itself in case of situational changes. Second, for an adaptive system to maintain its designated functionality, it must possess mechanisms that allow the system to keep its functional information and regulate its self-organizing process.;We develop and discuss a Self-Organizing System Design Framework or SOS Framework for the development of adaptive systems and draw insights for it from principles and concepts extracted from biology. In this dissertation we expand one facet of SOS by developing an artificial DNA-based Cellular Formation Representation framework (cFORE) for representing and constructing artificial systems in a manner which mimics biological systems. One of the key issues in achieving high adaptability in artificial systems is how to dynamically capture, represent and apply design information pertaining to the designed functions and changing environmental situations. Biological DNA in natural systems plays the key role in keeping, maintaining and transferring such "design information" within and between individuals. Resembling biological DNA, we developed an "artificial DNA" called dDNA (Design DNA) which maintains design information and provides an avenue for generating new designs adaptively. In this dissertation, we present a discussion of the overall SOS framework along with the modeling of its first facet, cFORE. A simple case example along with a more detailed multi-agent based computer simulation study is provided to demonstrate the power of SOS and cFORE towards the development of mechanical self-organizing cellular adaptive systems.