Conference Proceeding

# An enhanced statistical approach for evolutionary algorithm comparison.

01/2008; In proceeding of: Genetic and Evolutionary Computation Conference, GECCO 2008, Proceedings, Atlanta, GA, USA, July 12-16, 2008
Source: DBLP
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• ##### Conference Proceeding: Performance comparison of two evolutionary schemes
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ABSTRACT: The performance of a deterministic algorithm is judged by its computational complexity. But an evolutionary algorithm being probabilistic in nature, its convergence has some probability associated with it. In the present paper, we try to locate the parameters that control the performance of an evolutionary algorithm. In this formulation, we make some assumptions, under which the performance comparison is possible. Moreover we propose a notion of characteristic polynomial which gives a measure of the performance of an evolutionary scheme. The area under the curve of the characteristic polynomial over the range 0 to 1 is equivalently shown to represent the performance criterion
Pattern Recognition, 1996., Proceedings of the 13th International Conference on; 09/1996
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##### Article: Bootstrap Methods: Another Look at the Jackknife
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ABSTRACT: We discuss the following problem: given a random sample $\mathbf{X} = (X_1, X_2, \cdots, X_n)$ from an unknown probability distribution $F$, estimate the sampling distribution of some prespecified random variable $R(\mathbf{X}, F)$, on the basis of the observed data $\mathbf{x}$. (Standard jackknife theory gives an approximate mean and variance in the case $R(\mathbf{X}, F) = \theta(\hat{F}) - \theta(F), \theta$ some parameter of interest.) A general method, called the "bootstrap," is introduced, and shown to work satisfactorily on a variety of estimation problems. The jackknife is shown to be a linear approximation method for the bootstrap. The exposition proceeds by a series of examples: variance of the sample median, error rates in a linear discriminant analysis, ratio estimation, estimating regression parameters, etc.
The Annals of Statistics 01/1979; · 2.53 Impact Factor
• ##### Article: Nonparametric Estimates of Standard Error: The Jackknife, the Bootstrap and Other Methods
Biometrika 01/1981; 68(3). · 1.65 Impact Factor