A Memetic Differential Evolution Approach in Noisy Optimization

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dc.contributor.author Mininno, Ernesto en
dc.contributor.author Neri, Ferrante en
dc.date.accessioned 2012-08-14T10:59:23Z
dc.date.available 2012-08-14T10:59:23Z
dc.date.issued 2010-06
dc.identifier.citation Mininno, E. and Neri, F. (2010) A Memetic Differential Evolution Approach in Noisy Optimization. Memetic Computing Journal, Springer, 2 (2), pp. 111-135 en
dc.identifier.issn 1865-9284
dc.identifier.uri http://hdl.handle.net/2086/6825
dc.description.abstract This paper proposes amemetic approach for solving complex optimization problems characterized by a noisy fitness function. The proposed approach aims at solving highly multivariate and multi-modal landscapes which are also affected by a pernicious noise. The proposed algorithm employs a Differential Evolution framework and combines within this three additional algorithmic components. A controlled randomization of scale factor and crossover rate are employed which should better handle uncertainties of the problem and generally enhance performance of the Differential Evolution. Two combined local search algorithms applied to the scale factor, during offspring generation, should enhance performance of the Differential Evolution framework in the case of multi-modal and high dimensional problems. An on-line statistical test aims at assuring that only strictly necessary samples are taken and that all pairwise selections are properly performed. The proposed algorithm has been tested on a various set of test problems and its behavior has been studied, dependent on the dimensionality and noise level. A comparative analysis with a standard Differential Evolution, a modern version of Differential Evolution employing randomization of the control parameters and four metaheuristics tailored to optimization in a noisy environment has been carried out. One of these metaheuristics is a classical algorithm for noisy optimization while the other three are modern Differential Evolution based algorithms for noisy optimization which well represent the state-of-theart in the field. Numerical results show that the proposed memetic approach is an efficient and robust alternative for various and complex multivariate noisy problems and can be exported to real-world problems affected by a noise whose distribution can be approximated by a Gaussian distribution. en
dc.language.iso en en
dc.publisher Springer en
dc.subject differential evolution en
dc.subject scale factor local search en
dc.subject noise analysis en
dc.subject noisy optimization en
dc.title A Memetic Differential Evolution Approach in Noisy Optimization en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1007/s12293-009-0029-4
dc.researchgroup Centre for Computational Intelligence en

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