Retaining Experience and Growing Solutions

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

@Misc{oai:arXiv.org:1505.01474,
  author =       "Robyn Ffrancon",
  title =        "Retaining Experience and Growing Solutions",
  year =         "2015",
  month =        may # "~06",
  abstract =     "Generally, when genetic programming (GP) is used for
                 function synthesis any valuable experience gained by
                 the system is lost from one problem to the next, even
                 when the problems are closely related. With the aim of
                 developing a system which retains beneficial experience
                 from problem to problem, this paper introduces the
                 novel Node-by-Node Growth Solver (NNGS) algorithm which
                 features a component, called the controller, which can
                 be adapted and improved for use across a set of related
                 problems. NNGS grows a single solution tree from root
                 to leaves. Using semantic backpropagation and acting
                 locally on each node in turn, the algorithm employs the
                 controller to assign subsequent child nodes until a
                 fully formed solution is generated. The aim of this
                 paper is to pave a path towards the use of a neural
                 network as the controller component and also,
                 separately, towards the use of meta-GP as a mechanism
                 for improving the controller component. A
                 proof-of-concept controller is discussed which
                 demonstrates the success and potential of the NNGS
                 algorithm. In this case, the controller constitutes a
                 set of hand written rules which can be used to
                 deterministically and greedily solve standard Boolean
                 function synthesis benchmarks. Even before employing
                 machine learning to improve the controller, the
                 algorithm vastly outperforms other well known recent
                 algorithms on run times, maintains comparable solution
                 sizes, and has a 100percent success rate on all Boolean
                 function synthesis benchmarks tested so far.",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1505.01474",
  keywords =     "genetic algorithms, genetic programming, computer
                 science - neural and evolutionary computing",
  URL =          "http://arxiv.org/abs/1505.01474",
}

Genetic Programming entries for Robyn Ffrancon

Citations