Quantum-Inspired Linear Genetic Programming

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

@PhdThesis{MotaDias:doctorate,
  author =       "Douglas {Mota Dias}",
  title =        "Quantum-Inspired Linear Genetic Programming",
  school =       "Engenharia Eletrica, Pontificia Universidade Catolica
                 do Rio de Janeiro -- PUC-Rio",
  year =         "2010",
  address =      "Brasil",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming,
                 quantum-inspired evolutionary algorithms{"}",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17544@2",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/17544/17544_1.PDF",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/17544/17544_2.PDF",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/17544/17544_3.PDF",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/17544/17544_4.PDF",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/17544/17544_5.PDF",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/17544/17544_6.PDF",
  URL =          "http://www.maxwell.lambda.ele.puc-rio.br/17544/17544_7.PDF",
  size =         "97 pages",
  abstract =     "The superior performance of quantum algorithms in some
                 specific problems lies in the direct use of quantum
                 mechanics phenomena to perform operations with data on
                 quantum computers. This feature has originated a new
                 approach, named Quantum-Inspired Computing, whose goal
                 is to create classic algorithms (running on classical
                 computers) that take advantage of quantum mechanics
                 principles to improve their performance. In this sense,
                 some quantum-inspired evolutionary algorithms have been
                 proposed and successfully applied in combinatorial and
                 numerical optimisation problems, presenting a superior
                 performance to that of conventional evolutionary
                 algorithms, by improving the quality of solutions and
                 reducing the number of evaluations needed to achieve
                 them. To date, however, this new paradigm of quantum
                 inspiration had not yet been applied to Genetic
                 Programming (GP), a class of evolutionary algorithms
                 that aims the automatic synthesis of computer programs.
                 This thesis proposes, develops and tests a novel model
                 of quantum-inspired evolutionary algorithm named
                 Quantum-Inspired Linear Genetic Programming (QILGP) for
                 the evolution of machine code programs. Linear Genetic
                 Programming is so named because each of its individuals
                 is represented by a list of instructions (linear
                 structures), which are sequentially executed. The
                 contributions of this work are the study and
                 formulation of the novel use of quantum inspiration
                 paradigm on evolutionary synthesis of computer
                 programs. One of the motivations for choosing by the
                 evolution of machine code programs is because this is
                 the GP approach that, by offering the highest speed of
                 execution, makes feasible large-scale experiments. The
                 proposed model is inspired on multi-level quantum
                 systems and uses the qudit as the basic unit of quantum
                 information, which represents the superposition of
                 states of such a system. The model's operation is based
                 on quantum individuals, which represent a superposition
                 of all programs of the search space, whose observation
                 leads to classical individuals and programs
                 (solutions). The tests use symbolic regression and
                 binary classification problems to evaluate the
                 performance of QILGP and compare it with the AIMGP
                 model (Automatic Induction of Machine Code by Genetic
                 Programming), which is currently considered the most
                 efficient GP model to evolve machine code, as cited in
                 numerous references in this field. The results show
                 that Quantum-Inspired Linear Genetic Programming
                 (QILGP) presents superior overall performance in these
                 classes of problems, by achieving better solutions
                 (smallest error) from a smaller number of evaluations,
                 with the additional advantage of using a smaller number
                 of parameters and operators that the reference model.
                 In comparative tests, the model shows average
                 performance higher than that of the reference model for
                 all case studies, achieving errors 3-31percent lower in
                 the problems of symbolic regression, and 36-39percent
                 in the binary classification problems. This research
                 concludes that the quantum inspiration paradigm can be
                 a competitive approach to efficiently evolve programs,
                 encouraging the improvement and extension of the model
                 presented here, as well as the creation of other models
                 of quantum-inspired genetic programming. model. In
                 comparative tests, the model shows average performance
                 higher than that of the reference model for all case
                 studies, achieving errors 3-31percent lower in the
                 problems of symbolic regression, and 36-39percent in
                 the binary classification problems. This research
                 concludes that the quantum inspiration paradigm can be
                 a competitive approach to efficiently evolve programs,
                 encouraging the improvement and extension of the model
                 presented here, as well as the creation of other models
                 of quantum-inspired genetic programming.",
  notes =        "Supervised by Marco Aurelio. In Portuguese",
}

Genetic Programming entries for Douglas Mota Dias

Citations