Evaluation of the genetic algorithm parameters on the optimization performance: a case study on pump-and-treat remediation design

Gungor-Demirci, Gamze
Aksoy, Ayşegül
In this study, the impacts of different crossover and encoding schemes on the performance of a genetic algorithm (GA) in finding optimal pump-and-treat (P&T) remediation designs are investigated. For this purpose, binary and Gray encodings of the decision variables are tested. Uniform and two-point crossover schemes are evaluated for two different crossover probabilities. Analysis is performed for two P&T system optimization scenarios. Results show that uniform crossover operator with Gray encoding outperforms the other alternatives for the complex problem with higher number of decision variables. On the other hand, when a simpler problem, which had a lower number of decision variables, is solved, the efficiency of GA is independent of the encoding and crossover schemes.


Estimation in bivariate nonnormal distributions with stochastic variance functions
Tiku, Moti L.; İslam, Muhammed Qamarul; SAZAK, HAKAN SAVAŞ (Elsevier BV, 2008-01-01)
Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions. Estimation of parameters in such situations is considered when the mean and variance of one variable is a linear and a positive function of the other variable. This is typically true of bivariate t distribution. The resulting estimators are found to be remarkably efficient. Hypothesis testing procedures are developed and shown to be robust and powerful. Real life examples are given.
An algorithm to analyze stability of gene-expression patterns
Gebert, J; Latsch, M; Pickl, SW; Weber, Gerhard Wilhelm; Wunschiers, R (Elsevier BV, 2006-05-01)
Many problems in the field of computational biology consist of the analysis of so-called gene-expression data. The successful application of approximation and optimization techniques, dynamical systems, algorithms and the utilization of the underlying combinatorial structures lead to a better understanding in that field. For the concrete example of gene-expression data we extend an algorithm, which exploits discrete information. This is lying in extremal points of polyhedra, which grow step by step, up to a...
Robust estimation and hypothesis testing under short-tailedness and inliers
Akkaya, Ayşen (Springer Science and Business Media LLC, 2005-06-01)
Estimation and hypothesis testing based on normal samples censored in the middle are developed and shown to be remarkably efficient and robust to symmetric short-tailed distributions and to inliers in a sample. This negates the perception that sample mean and variance are the best robust estimators in such situations (Tiku, 1980; Dunnett, 1982).
Analysis of Covariance with Non-normal Errors
ŞENOĞLU, BİRDAL; Avcioglu, Mubeccel Didem (Wiley, 2009-12-01)
P>Analysis of covariance techniques have been developed primarily for normally distributed errors. We give solutions when the errors have non-normal distributions. We show that our solutions are efficient and robust. We provide a real-life example.
Estimation and hypothesis testing in multivariate linear regression models under non normality
İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01)
This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Citation Formats
G. Gungor-Demirci and A. Aksoy, “Evaluation of the genetic algorithm parameters on the optimization performance: a case study on pump-and-treat remediation design,” TOP, pp. 303–320, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36100.