Artificial Evolution of Arbitrary Self-Replicating Cellular Automata

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Zhijian_Pan:thesis,
  author =       "Zhijian Pan",
  title =        "Artificial Evolution of Arbitrary Self-Replicating
                 Cellular Automata",
  school =       "University of Maryland, College Park",
  year =         "2007",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://phdtree.org/pdf/25435445-artificial-evolution-of-arbitrary-self-replicating-cellular-automata/",
  URL =          "http://hdl.handle.net/1903/7404",
  URL =          "http://drum.lib.umd.edu/bitstream/handle/1903/7404/umi-umd-4824.pdf",
  size =         "241 pages",
  abstract =     "Since John von Neumann's seminal work on developing
                 cellular automata models of self-replication, there
                 have been numerous computational studies that have
                 sought to create self-replicating structures or
                 machines. Cellular automata (CA) has been the most
                 widely used method in these studies, with manual
                 designs yielding a number of specific self-replicating
                 structures. However, it has been found to be very
                 difficult, in general, to design local state-transition
                 rules that, when they operate concurrently in each cell
                 of the cellular space, produce a desired global
                 behaviour such as self-replication. This has greatly
                 limited the number of different self-replicating
                 structures designed and studied to date. In this
                 dissertation, I explore the feasibility of overcoming
                 this difficulty by using genetic programming (GP) to
                 evolve novel CA self-replication models. I first
                 formulate an approach to representing structures and
                 rules in cellular automata spaces that is amenable to
                 manipulation by the genetic operations used in GP.
                 Then, using this representation, I demonstrate that it
                 is possible to create a replicator factory that
                 provides an unprecedented ability to automatically
                 generate a whole class of new self-replicating
                 structures and that allows one to systematically
                 investigate the properties of replicating structures as
                 one varies the initial configuration, its size, shape,
                 symmetry, and allowable states. This approach is then
                 extended to incorporate multi-objective fitness
                 criteria, resulting in production of diversified
                 replicators. For example, this allows generation of
                 target structures whose complexity greatly exceeds that
                 of the seed structure itself. Finally, the extended
                 multi-objective replicator factory is further
                 generalized into a structure/rule co-evolution model,
                 such that replicators with unspecified seed structures
                 can also be concurrently evolved, resulting in
                 different structure/rule combinations and having the
                 capability of not only replicating but also carrying
                 out a secondary pre-specified task with different
                 strategies. I conclude that GP provides a powerful
                 method for creating CA models of self-replication.",
  notes =        "Supervisor: James Reggia",
}

Genetic Programming entries for Zhijian Pan

Citations