Local Evolutionary Search Enhancement by Random Memorizing
نویسنده
چکیده
| For the calibration of laser induced plasma spectrometers robust and eecient local search methods are required. Therefore, several local optimizers from nonlinear optimization, random search and evolutionary computation are compared. It is shown that evolutionary algorithms are superior with respect to reliability and eeciency. To enhance the local search of an evolutionary algorithm a new method of random memorizing is introduced. This method is applied to one of the most simple evolutionary algorithm, the (1+1)-Evolution Strategy. It leads to a substantial gain in eeciency for a reliable local search. Finally, laser induced plasma spectroscopy and the calibration of a real example are scetched.
منابع مشابه
On the Benefits of Random Memorizing in Local Evolutionary Search
For the calibration of laser induced plasma spectrometers robust and eecient local search methods are required. Therefore, several local optimizers from nonlinear optimization, random search and evolutionary computation are compared. It is shown that evolutionary algorithms are superior with respect to reliability and eeciency. To enhance the local search of an evolutionary algorithm a new meth...
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