Programacion Genetica de Heuristicas para Planificacion

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

@PhdThesis{aler:thesis,
  author =       "Ricardo Aler Mur",
  title =        "Programacion Genetica de Heuristicas para
                 Planificacion",
  school =       "Facultad de Informatica de la Universidad Politecnica
                 de Madrid",
  year =         "1999",
  address =      "Spain",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Planning,
                 Problem Solving, Rule Based System",
  URL =          "http://oa.upm.es/1101/1/10199907.pdf",
  size =         "167 pages",
  abstract =     "The aim of this thesis is to use and extend the
                 machine learning genetic programming (GP) paradigm to
                 learn control knowledge for domain independent
                 planning. GP will be used as a standalone technique and
                 as part of a multi-strategy system.

                 Planning is the problem of finding a sequence of steps
                 to transform an initial state in a final state. Finding
                 a correct plan is NP-hard. A solution proposed by
                 Artificial Intelligence is to augment a domain
                 independent planner with control knowledge, to improve
                 its efficiency. Machine learning techniques are used
                 for that purpose. However, although a lot has been
                 achieved, the domain independent planning problem has
                 not been solved completely, therefore there is still
                 room for research.

                 The reason for using GP to learn planning control
                 knowledge is twofold. First, it is intended for
                 exploring the control knowledge space in a less biased
                 way than other techniques. Besides, learning search
                 control knowledge with GP will consider the planning
                 system, the domain theory, planning search and
                 efficiency measures in a global manner, all at the same
                 time. Second, GP flexibility will be used to add useful
                 biases and characteristics to another learning method
                 that lacks them (that is, a multi-strategy GP based
                 system). In the present work, Prodigy will be used as
                 the base planner and Hamlet will be used as the
                 learning system to which useful characteristics will be
                 added through GP. In other words, GP will be used to
                 solve some of Hamlet limitations by adding new
                 biases/characteristics to Hamlet.

                 In addition to the main goal, this thesis will design
                 and experiment with methods to add background knowledge
                 to a GP system, without modifying its basic algorithm.
                 The first method seeds the initial population with
                 individuals obtained by another method (Hamlet).
                 Actually, this is the multi-strategy system discussed
                 in the later paragraph. The second method uses a new
                 genetic operator (instance based crossover) that is
                 able to use instances/examples to bias its search, like
                 other machine learning techniques.

                 To test the validity of the methods proposed, extensive
                 empirical and statistical validation will be carried
                 out.",
  notes =        "In Spanish: Genetic Programming of Heuristics for
                 Planning School of Computer Science at Polytechnic
                 University of Madrid Author: Ricardo Aler Mur
                 Supervisors: Daniel Borrajo Millan and Pedro Isasi
                 Vinuela

                 ",
}

Genetic Programming entries for Ricardo Aler Mur

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