Estimation of the parameters of a nonlinear sum of exponentials model is an important and well studied problem in time series analysis. The sum of exponentials model finds application in modeling various physical phen...
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Estimation of the parameters of a nonlinear sum of exponentials model is an important and well studied problem in time series analysis. The sum of exponentials model finds application in modeling various physical phenomena in a wide variety of real life applications. The problem of finding the nonlinear least squares estimates in well known to be numerically difficult. In this paper, we propose an elitist generationalgenetic algorithm based iterative procedure for computing the nonlinear least squares estimates. Simulation studies and real life data fitting examples indicate satisfactory performance of the proposed technique. (C) 2011 Elsevier Ltd. All rights reserved.
The paper addresses the issue of automatic generation of music excerpts. The character of the problem makes it suitable for various kinds of evolutionary Computation algorithms. We introduce a special method of indire...
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ISBN:
(纸本)9783642011283
The paper addresses the issue of automatic generation of music excerpts. The character of the problem makes it suitable for various kinds of evolutionary Computation algorithms. We introduce a special method of indirect melodic representation that allows simple application of standard search operators like crossover and mutation with no repair mechanisms necessary. A method is proposed for automatic evaluation of melodies based upon a corpus of manually coded examples, such as classical music opi. Various kinds of genetic Algorithm (GA) were tested against this e.g., generational GAs and steady-state GAs. The results show the ability of the method for further applications in the domain of automatic music composition.
This paper compares three evolutionary computation techniques, namely Steady-State geneticalgorithms, Evolutionary Strategies and Differential Evolution for the Unit Commitment Problem. The comparison. is based on a ...
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This paper compares three evolutionary computation techniques, namely Steady-State geneticalgorithms, Evolutionary Strategies and Differential Evolution for the Unit Commitment Problem. The comparison. is based on a set of experiments conducted on benchmark datasets as well as on real-world data obtained from the Turkish Interconnected Power System. The results of two state-of-the-art evolutionary approaches, namely a generationalgenetic Algorithm and a Memetic Algorithm for the same benchmark datasets are also included in. the paper for comparison. The tests show that Differential Evolution is the best performer among all approaches on the test data used in the paper. The performances of the other two evolutionary algorithms are also comparable to Differential Evolution. and the results of the algorithms taken from literature showing that all EA approaches tested here are applicable to the Unit Commitment Problem. The results of this experimental study are very promising and promote further study.
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