Hierarchical Learning with Procedural Abstraction Mechanisms

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

@PhdThesis{rosca:thesis,
  author =       "Justinian P. Rosca",
  title =        "Hierarchical Learning with Procedural Abstraction
                 Mechanisms",
  school =       "Department of Computer Science, The College of Arts
                 and Sciences, University of Rochester",
  year =         "1997",
  address =      "Rochester, NY 14627, USA",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Procedural
                 Representations, Stochastic Search, Reinforcement
                 Learning, Hierarchical Abstraction, Problem
                 Decomposition, Generalization and Complexity",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/jrphdd.ps.gz",
  size =         "260 pages",
  abstract =     "The emerging field called evolutionary computation
                 (EC) consists of the design and analysis of
                 probabilistic algorithms inspired by the principles of
                 natural selection and variation. Genetic Programming
                 (GP) is one subfield of EC that emphasizes (1) the
                 capability to discover and exploit intrinsic
                 characteristics of the problem domain and (2) the
                 flexibility to adapt the shape and complexity of
                 structures manipulated in order to fit the specificity
                 of the application. GP appears to be a robust
                 stochastic search technique that can be applied to
                 complex design, control, and knowledge discovery
                 applications. However, the computational load when
                 applying it to non-trivial problems is considerable.
                 The problem of understanding and controlling the GP
                 technique is notoriously difficult.

                 This dissertation describes theoretical and
                 experimental work aimed at (1) understanding the
                 characteristics and limitations of GP search, and (2)
                 extending the capabilities of GP in order to cope with
                 problems of increasing complexity. For the first
                 challenge, we focus on the properties and the role of
                 the problem representation and analyze the implicit
                 biases when manipulating variable length structures
                 represented as trees. We analyze uniform random
                 sampling on the space of tree structures to offer
                 insights into the role of procedural representations.
                 We describe the dynamics of a useful structural
                 property of representations called rooted tree
                 schemata. This demonstrates the role played by the
                 adaptive complexity of evolved structures. We also
                 capture the dynamics of behaviors using the concept of
                 population entropy.

                 The solution to the second challenge relies on the
                 observation that on non-trivial problems GP essentially
                 assembles and adapts the bits and pieces of a huge
                 monolithic model. We propose, instead, that the
                 learning system provide abstraction mechanisms for
                 adaptively creating and exploiting modularity and
                 problem decomposition. We evaluate previous steps in
                 this direction by looking at GP search biases and
                 complexity measures of solutions, such as expanded and
                 descriptional complexity, and we characterize the types
                 of modular structures that would result in a minimum
                 description length of solutions. Then we describe two
                 GP extension approaches for creating and exploiting
                 procedural abstractions in the form of subroutines in
                 order to facilitate scale-up. The first extension,
                 called Adaptive Representation (AR) is a heuristic
                 modular learning approach based on the discovery of
                 hierarchies of subroutines. The second extension,
                 called Evolutionary Divide-and-Conquer (EDC) views the
                 population as a pool of candidates for selecting a team
                 that solve the problem. Both techniques extract simple
                 or {"}natural{"} relationships and build modular
                 representations to explain data. The techniques are
                 brought to life in several increasingly complex
                 algorithms.

                 The effects of embedding procedural and data
                 abstraction mechanisms in the learning process are
                 evaluated from several perspectives, such as reuse of
                 code or structure, automatic problem decomposition,
                 generalization, and automatic discovery of features on
                 several challenging problems. AR was successfully
                 applied to the intelligent control of an agent in a
                 dynamic and non-deterministic environment. Ideas are
                 further extended for designing graphical models. EDC
                 was applied to regression problems characterized by
                 complex input spaces.",
  notes =        "

                 ",
}

Genetic Programming entries for Justinian Rosca

Citations