Object-Oriented Ontogenetic Programming: Breeding Computer Programs that Work Like Multicellular Creatures

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Peter Schmutter",
  title =        "Object-Oriented Ontogenetic Programming: Breeding
                 Computer Programs that Work Like Multicellular
  school =       "University of Dortmund, Germany",
  year =         "2002",
  type =         "Diploma thesis",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming,
                 Object-Oriented Ontogenetic Programming, Multicellular
                 Programming, Swarm-Programming, Evolutionof Distributed
                 Intelligence, Gene Regulation, Embryology, Amorphous
                 Computing, Multiagent Systems",
  URL =          "http://www.ooop.org/publications/thesis/",
  URL =          "http://www.evodi.org/publications/ooop-thesis.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/537098.html",
  broken =       "http://eldorado.uni-dortmund.de/0x81d98002_0x00054f1d",
  size =         "100 pages",
  abstract =     "As the research field called Genetic Programming has
                 shown during the last decade, it is possible not only
                 to write computer programs by hand but also to let the
                 computer itself develop programs that solve given
                 problems. This is achieved by simulating natural
                 evolution on the computer for breeding programs that
                 are well adapted to a specific problem environment. The
                 use of mechanisms found in nature can lead to solutions
                 to complex problems that by far outperform any man-made
                 approaches. The reasons are that complex problems often
                 are difficult to solve analytically and many other
                 possible approaches are not accessible to the human way
                 of thinking. The use of the mechanisms of evolution
                 based on genetic variation and survival of the fittest
                 is only one example. Another example are Artificial
                 Neural Networks that imitate clusters of nervous cells
                 and their interactions for solving difficult problems
                 (inspired among others by the human brain).

                 The here presented work explores a different and new
                 approach to adopting problem solving methods found in
                 nature. It uses the natural cell control mechanism
                 called Gene Regulation that according to modern
                 molecular genetics is the basis of the cooperation
                 between and differentiation into all the different
                 cells in living creatures. The most astonishing example
                 of self-organization between simple units that
                 cooperate to solve complex problems is not the
                 interaction between nervous cells on the basis of
                 mutual electrical activation through explicit and
                 directed connections. It is the interaction between all
                 kinds of cells in a living creature which is based on
                 the diffusion of messages in the form of produced
                 substances. This interaction is much more powerful and
                 flexible than the neural interaction because of many
                 reasons. The main reason is, that a cell in this
                 context is not only a simple unit which can have
                 different levels of activation, but it is a complex
                 system with many behavioural possibilities. The
                 communication between the cells not only bases on
                 different activation intensities but on many different
                 message types which (also depending their intensity)
                 can have very sophisticated effects on the behaviour of
                 a cell.

                 This new programming and control paradigm has been
                 combined with genetic programming for breeding
                 multicellular programs (which probably is the only
                 feasible way of producing them). The system that
                 implements this combination can not only be used to
                 create programs with a new modular structure which has
                 several advantages. It also is a great tool for
                 developing systems of cooperating autonomous units like
                 Amorphous Computers and Multiagent Systems.",

Genetic Programming entries for Peter Schmutter