Chemotaxis-based Spatial Self-Organization Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@PhdThesis{Bai_LingePhD,
  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",
}

Genetic Programming entries for Linge Bai

Citations