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dc.contributor.authorMininno, Ernestoen
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorCupertino, Francescoen
dc.contributor.authorNaso, Daviden
dc.date.accessioned2012-04-03T11:10:59Z
dc.date.available2012-04-03T11:10:59Z
dc.date.issued2011
dc.identifier.citationMininno, E., Neri, F., Cupertino, F. and Naso, D. (2011), Compact Differential Evolution. IEEE Transactions on Evolutionary Computation, 15, (1), pp 32-54en
dc.identifier.issn1089-778X
dc.identifier.urihttp://hdl.handle.net/2086/5867
dc.description.abstractThis paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neuralnetwork-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental results on the real-world problem are very promising and allow us to think that cDE and future developments can be an efficient option for optimization in hardware environments characterized by limited memory.en
dc.language.isoenen
dc.subjectadaptive systemsen
dc.subjectcompact genetic algorithmsen
dc.subjectdifferential evolution (DE)en
dc.subjectestimation distribution algorithmsen
dc.titleCompact Differential Evolutionen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TEVC.2010.2058120
dc.researchgroupCentre for Computational Intelligenceen
dc.ref2014.selected1367395509_1210680252775_11_2
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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