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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Comparison of Various Evolutionary and Memetic Algorithms
verfasst von : Krisztián Balázs, János Botzheim, László T. Kóczy
Erschienen in: Integrated Uncertainty Management and Applications
Verlag: Springer Berlin Heidelberg
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Optimization methods known from the literature include gradient based techniques and evolutionary algorithms. The main idea of the former methods is to calculate the gradient of the objective function at the actual point and then to step towards better values according to this function value. Evolutionary algorithms imitate a simplified abstract model of evolution observed in the nature. Memetic algorithms traditionally combine evolutionary and other, e.g. gradient techniques to exploit the advantages of both methods. Our current research aims to discover the properties, especially the efficiency (i.e. the speed of convergence) of particular evolutionary and memetic algorithms. For this purpose the techniques are compared by applying them on several numerical optimization benchmark functions and on machine learning problems.