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Title: Automated evolutionary design of self-assembly and self-organising systems
Author: Terrazas Angulo, German
ISNI:       0000 0004 2684 8261
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2009
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Self-assembly and self-organisation are natural construction processes where the spontaneous formation of aggregates emerges throughout the progressive interplay of local interactions among its constituents. Made upon cooperative self-reliant components, self-assembly and self-organising systems are seen as distributed, not necessarily synchronous, autopoietic mechanisms for the bottom-up fabrication of supra-structures. The systematic understanding of how nature endows these autonomous components with sufficient ''intelligence'' to combine themselves to form useful aggregates brings challenging questions to science, answers to which have many potential applications in matters of life and technological advances. It is for this reason that the investigation to be presented along this thesis focuses on the automated design of self-assembly and self-organising systems by means of artificial evolution. Towards this goal, this dissertation embodies research on evolutionary algorithms applied to the parameters design of a computational model of self-organisation and the components design of a computational model of self-assembly. In addition, an analytical assessment combining correlation metrics and clustering, as well as the exploration of emergent patterns of cooperativity and the measurement of activity across evolution, is made. The results support the research hypothesis that an adaptive process such as artificial evolution is indeed a suitable strategy for the automated design of self-assembly and self-organising systems where local interactions, homogeneity and both stochastic and discrete models of execution play a crucial role in emergent complex structures.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Q Science (General)