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dc.contributor.authorIacca, Giovannien
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorMininno, Ernestoen
dc.contributor.authorCaraffini, Fabioen
dc.date.accessioned2016-03-31T09:52:02Z
dc.date.available2016-03-31T09:52:02Z
dc.date.issued2012-04
dc.identifier.citationIacca, G., Caraffini, F., Neri, F. and Mininno, E. (2012) Applications of Evolutionnary Computation: EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES,EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, Malaga, Spain, April 11-13, 2012, Proceedings, chapter Robot Base Disturbance Optimization with Compact Differential Evolution Light, pp. 285-294en
dc.identifier.isbn9783642291784
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-642-29178-4_29
dc.identifier.urihttp://hdl.handle.net/2086/11738
dc.descriptionThis research is supported by the Academy of Finland, Akatemiatutkija 130600, Algorithmic Design Issues in Memetic Computing and Tutkijatohtori 140487, Algorithmic Design and Software Implementation: a Novel Optimization Platform.en
dc.description.abstractDespite the constant growth of the computational power in consumer electronics, very simple hardware is still used in space applications. In order to obtain the highest possible reliability, in space systems limited-power but fully tested and certified hardware is used, thus reducing fault risks. Some space applications require the solution of an optimization problem, often plagued by real-time and memory constraints. In this paper, the disturbance to the base of a robotic arm mounted on a spacecraft is modeled, and it is used as a cost function for an online trajectory optimization process. In order to tackle this problem in a computationally efficient manner, addressing not only the memory saving necessities but also real-time requirements, we propose a novel compact algorithm, namely compact Differential Evolution light (cDElight). cDElight belongs to the class of Estimation of Distribution Algorithms (EDAs), which mimic the behavior of population-based algorithms by means of a probabilistic model of the population of candidate solutions. This model has a more limited memory footprint than the actual population. Compared to a selected set of memory-saving algorithms, cDElight is able to obtain the best results, despite a lower computational overhead.en
dc.publisherSpringer Berlin Heidelbergen
dc.relation.ispartofseriesLecture Notes in Computer Science;
dc.subjectDifferential evolutionen
dc.subjectCompact algorithmsen
dc.subjectOptimisationen
dc.titleRobot Base Disturbance Optimization with Compact Differential Evolution Lighten
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligence
dc.peerreviewedYesen
dc.fundern/aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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