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billposting. For the evaluation of the performances of their method, they introduced the entropy and the edge correlation. In the proposed method we use MI, normalized mutual information (NMI) as to fitness function for the genetic algorithm (GA) and we

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Feistauer, M. et al., Springer Verlag, 2004, pp. 459–470. Cantu-Paz, E. Designing Efficient Master-Slave Parallel Genetic Algorithms, Genetic Programming 1998: Proceedings of the Third Annual Conference , University of

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Feistauer, M. et al., Springer Verlag, 2004, pp. 459–470. Marco-Blaszka N., Désidéri J. Numerical solution of optimization test cases by genetic algorithms, INRIA Research Report , 3622, 1999

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, No. 3 , 2012 , pp. 15 – 21 . [5] Goldberg D. E. Genetic algorithms in search optimization and machine

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, Essentials of Metaheuristics, Zeroth Edition, Online Version 0.3, 2009, available at: http://cs.gmu.edu/~sean/book/metaheuristics/ R.L. Haupt, S.E. Haupt , Practical Genetic Algorithms, John Wiley & Sons

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A method for optimization of a TLC separation based on use of a genetic algorithm is described. The procedure was tested by optimization of the reversed-phase HPTLC separation of a mixture of six pesticides and satisfactory optimum results were obtained. The performance of the genetic algorithm was tested by measurement of the number of generations, the population size, the mutation probability, and the crossover probability. Three separation criteria ( MRF , R * and R **) were examined as fitness functions. The genetic algorithm was compared with the simplex method.

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We investigated the potential application of the genetic algorithm in the analysis of X-ray fluorescence spectra from measurement of lead in bone. Candidate solutions are first designed based on the field knowledge and the whole operation, evaluation, selection, crossover and mutation, is then repeated until a given convergence criterion is met. An average-parameters based genetic algorithm is suggested to improve the fitting precision and accuracy. Relative standard deviation (RSD%) of fitting amplitude, peak position and width is 1.3-7.1, 0.009-0.14 and 1.4-3.3, separately. The genetic algorithm was shown to make a good resolution and fitting of K lines of Pb and g elastic peaks.

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Optimization of microwave-assisted extraction (MAE) as a method of sample preparation in thin-layer chromatographic (TLC) analysis of a herbicide mixture is described. The extraction was optimized with regard to amount of solvent, duration of microwave extraction, and temperature. In the proposed method the experimental-design technique was used to design initial experiments and a genetic algorithm (GA) was used in the optimization procedure. The general objective was to test a mathematical tool which could facilitate optimization. The optimization procedure was tested in the TLC determination of a mixture of the herbicides atrazine and simazine; determination of recovery revealed results were satisfactory. The GA proved to be an optimization procedure which can be successfully applied to optimization of MAE experiments.

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A program is described which enables performing of genetic algorithms for the determination of two positive real parameters. These new types of procedures are tested on a software of determination of flame temperatures previously developed in a fully classic way. The genetic operators used are crossover and mutation. They perform operations on a binary coded form of the parameters. The goal of the present study consists in developing and optimizing a genetic determination of the parameters at a given temperature. We succeed in selecting the general architecture of the procedure and implementing it in our main software of calculation of flame temperature. We have chosen this pyrotechnic field of application because we knew the behaviour of the real parameters, so the debugging operations were easier.

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This article determines the operating conditions leading to maximum work in a regenerative cycle with an open feed water heater through a procedure that combines the use of artificial neural networks (ANNs) and genetic algorithms (GAs). Water is an active fluid in the thermodynamical cycle; an objective function is obtained by using vapor enthalpy (a nonlinear function of operating conditions). Utilizing classical methods for maximizing the objective function usually leads to suboptimal solutions. Therefore, this article uses ANNs to estimate the steam properties as a function of operating conditions and GAs to optimize the thermodynamical cycle. The operating conditions are chosen with the aim of gaining maximum work in a boiler for a specific heat. To estimate the thermodynamic properties, an ANN was used to provide the necessary data required in the GA calculation.

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