The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

David Camacho - One of the best experts on this subject based on the ideXlab platform.

  • Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

  • IEEE Congress on Evolutionary Computation - Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

David F. Barrero - One of the best experts on this subject based on the ideXlab platform.

  • Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

  • IEEE Congress on Evolutionary Computation - Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

Bonifacio Castaño - One of the best experts on this subject based on the ideXlab platform.

  • Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

  • IEEE Congress on Evolutionary Computation - Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

María D. R-moreno - One of the best experts on this subject based on the ideXlab platform.

  • Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

  • IEEE Congress on Evolutionary Computation - Effects of the lack of selective pressure on the expected run-time Distribution in genetic programming
    2013 IEEE Congress on Evolutionary Computation, 2013
    Co-Authors: David F. Barrero, María D. R-moreno, Bonifacio Castaño, David Camacho
    Abstract:

    Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal Distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time Distribution. In particular, we explore the influence of the selective pressure on the run-time Distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time Distribution well described by the Weibull Probability Distribution.

Chris P Tsokos - One of the best experts on this subject based on the ideXlab platform.

  • Statistical analysis and modeling of Red Tide blooms
    Nonlinear Studies, 2011
    Co-Authors: R.d. Wooten, Chris P Tsokos
    Abstract:

    The present study considers the random phenomenon that is Red Tide as found around the State of Florida. Among the many organism that make up Red Tide, Karenia Brevis is the organism commonly associated with an outbreak, the Probability Distribution which best describes the behavior of Karenia Brevis is the Weibull Probability Distribution. There are regional differences as well as regional relationships including delay effects. Recursion rates indicate a logistic growth model; however, additional information is needed before research can determine the effects of runoff on Red Tide blooms.

  • transmuted Weibull Distribution a generalization of theWeibull Probability Distribution
    European Journal of Pure and Applied Mathematics, 2011
    Co-Authors: Gokarna Aryal, Chris P Tsokos
    Abstract:

    In this article, the two parameter Weibull Probability Distribution is embedded in a larger family obtained by introducing an additional parameter. We generalize the two parameter Weibull Distribution using the quadratic rank transmutation map studied by Shaw et al. [ 9 ] to develop a transmuted Weibull Distribution. We provide a comprehensive description of the mathematical properties of the subject Distribution along with its reliability behavior. The usefulness of the transmuted Weibull Distribution for modeling reliability data is illustrated using real data.

  • record values from half logistics and inverse Weibull Probability Distribution functions
    Neural Parallel & Scientific Computations archive, 2008
    Co-Authors: Alfred K Mbah, Chris P Tsokos
    Abstract:

    Let X1, X2,..., Xn be a sequence of independent and identically distributed random variables with cumulative Distribution function F(x). Denote XL(n) = min{X1, X2,..., Xn}, n = 2, 3,.... Xj is a lower record of {Xn} if and only if XL(j) < XL(j-1), j = 2, 3,... and XL(1) = X1. An analogous definition of records deals with upper record values. By definition, X1 is an upper as well as lower record value. The subject of the present paper is to introduce the Half logistic and the Inverse Weibull Probability Distributions when applied in studying the performance and behavior with respect to records. In addition to developing the analytical structures, we illustrate the usefulness of the results using environmental and sports data.

  • estimation of the three parameter Weibull Probability Distribution
    Mathematics and Computers in Simulation, 1995
    Co-Authors: Hongzhu Qiao, Chris P Tsokos
    Abstract:

    The aim of the present paper is to propose an algorithm to easily obtain good estimates of the three parameter Weibull Distribution. Our proposed procedure is given in eight steps and it depends on the Simple Iteration Procedure, which always converges, converges fast and does not depend on any conditions, whatsoever, that has been developed by the authors for the two parameter Weibull model. Numerical examples will be given to illustrate the effectiveness of our proposed statistical procedure. Finally, we address the issue of what we lose in characterizing the probabilistic behavior of a certain phenomenon with a two parameter Weibull model when in fact the true characterization calls for a three parameter Weibull model. We use the concept of percentiles, cumulative Distribution function and graphical presentations to answer the above questions.

  • parameter estimation of the Weibull Probability Distribution
    Mathematics and Computers in Simulation, 1994
    Co-Authors: Hongzhu Qiao, Chris P Tsokos
    Abstract:

    Newton—Raphson's method plays a fundamental role in the maximum likelihood estimation of the two parameters of the Weibull Probability Distribution. It is well known that the method depends on the initial point of the iterative process and the iteration does not always converge.