Algorithm Evolution with Internal Reinforcement for Signal Understanding

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Astro Teller",
  title =        "Algorithm Evolution with Internal Reinforcement for
                 Signal Understanding",
  school =       "School of Computer Science, Carnegie Mellon
  year =         "1998",
  address =      "Pittsburgh, USA",
  month =        "5 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  size =         "5.9 Mbytes, 166 pages",
  abstract =     "Automated program evolution has existed in some form
                 for almost forty years. Signal understanding (e.g.,
                 signal classification) has been a scientific concern
                 for longer than that. Generating a general machine
                 learning signal understanding system has more recently
                 attracted considerable research interest. First, this
                 thesis defines and creates a general machine learning
                 approach for signal understanding independent of the
                 signal's type and size. This is accomplished through an
                 evolutionary strategy of signal understanding programs
                 that is an extension of genetic programming. Second,
                 this thesis introduces a suite of sub-mechanisms that
                 increase the power of genetic programming and
                 contribute to the understanding of the learning
                 technique developed. The central algorithmic innovation
                 of this thesis is the process by which a novel
                 principled credit-blame assignment is introduced and
                 incorporated into the evolution of algorithms, thus
                 improving the evolutionary process. This principled
                 credit-blame assignment is done through a new program
                 representation called neural programming and applied
                 through a set of principled processes collectively
                 called internal reinforcement in neural programming.
                 This thesis concentrates on these algorithmic
                 innovations in real world signal domains where the
                 signals are typically large and/or poorly understood.
                 This evolutionary learning of algorithms takes place in
                 PADO, a system developed in this thesis for ``parallel
                 algorithm discovery and orchestration'' and as a
                 demonstrably effective strategy for divide-and-conquer
                 in signal classification domains. This thesis includes
                 an extensive empirical evaluation of the techniques
                 developed in a rich variety of real-world signals. The
                 results obtained demonstrate, among other things, the
                 effectiveness of principled credit-blame assignment in
                 algorithm evolution. This work is unique in three
                 aspects. No other currently existing system can learn
                 to classify or otherwise ``symbolize'' signals with no
                 space or size penalties for the signal's size or type.
                 No other system based on genetic programming currently
                 exists that purposefully generates and orchestrates a
                 variety of experts along problem specific lines. And,
                 most centrally, the thesis introduces the first
                 analytically sound mechanism for explaining and
                 reinforcing specific parts of an evolving program. The
                 goal of this thesis is to argue, explain, and
                 demonstrate how representation and search are
                 intimately connected in evolutionary computation and to
                 address these dual concerns in the context of the
                 evolution of Turing complete programs. Ideally, this
                 thesis will inspire future research in this same area
                 and along similar lines.",
  notes =        "Publication Number: CMU-CS-98-132",

Genetic Programming entries for Astro Teller