- author = "Linge Bai",
- title = "Chemotaxis-based Spatial Self-Organization Algorithms",
- school = "Department of Computer Science, Drexel University",
- year = "2014",
- address = "Philadelphia, USA",
- month = aug,
- keywords = "genetic algorithms, genetic programming, Chemotaxis, Self-organizing systems",
-
URL = "
https://www.cs.drexel.edu/~david/Abstracts/bai_phd-abs.html",
-
URL = "http://hdl.handle.net/1860/idea:6006",
-
URL = "
https://idea.library.drexel.edu/islandora/object/idea%3A6006/datastream/OBJ/download/Chemotaxis-based_spatial_self-organization_algorithms.pdf",
- size = "164 pages",
-
abstract = "Self-organization is a process that increases the
order of a system as a result of local interactions
among low-level, simple components, without the
guidance of an outside source. Spatial
self-organization is a process in which shapes and
structures emerge at a global level from collective
movements of low level shape primitives. Spatial
self-organization is a stochastic process, and the
outcome of the aggregation cannot necessarily be
guaranteed. Despite the inherent ambiguity,
self-organizing complex systems arise everywhere in
nature. Motivated by the ability of living cells to
form specific shapes and structures, we develop two
self-organizing systems towards the ultimate goal of
directing the spatial self-organizing process. We first
develop a self-sorting system composed of a mixture of
cells. The system consistently produces a sorted
structure. We then extend the sorting system to a
general shape formation system. To do so, we introduce
morphogenetic primitives (MP), defined as software
agents, which enable self-organizing shape formation of
user-defined structures through a chemotaxis
paradigm.
One challenge that arises from the shape formation process is that the process may form two or more stable final configurations. In order to direct the self-organizing process, we find a way to characterize the macroscopic configuration of the MP swarm. We demonstrate that statistical moments of the primitives locations can successfully capture the macroscopic structure of the aggregated shape. We do so by predicting the final configurations produced by our spatial self-organization system at an early stage in the process using features based on the statistical moments. At the next stage, we focus on developing a technique to control the outcome of bifurcating aggregations. We identify thresholds of the moments and generate biased initial conditions whose statistical moments meet the thresholds. By starting simulations with biased, random initial configurations, we successfully control the aggregation for a number of swarms produced by the agent-based shape formation system. This thesis demonstrates that chemotaxis can be used as a paradigm to create an agent-based spatial self-organization system. Furthermore, statistical moments of the swarm can be used to robustly predict and control the outcomes of the aggregation process.",
-
notes = "3.5.2 Chemical Field Evolution via Genetic Programming
https://www.cs.drexel.edu/~david/geom_biomed_comp.html#morph_prim
Supervisor David Breen",
