00949nam0 22002411i 450 SUN002276320050405120000.020040906d1977 |0itac50 baitaIT|||| |||||ˆIl ‰rito religiosostudi psicoanaliticiTheodor Reik[prefazione di Sigmund Freudtraduzione di Franco Ferrarotti]TorinoBoringhieri1977359 p.19 cm.TorinoSUNL000001Reik, TheodorSUNV018970161229BoringhieriSUNV001017650ITSOL20181109RICASUN0022763UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA16 CONS 837 16 VS 2261 UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIAIT-CE0119VS2261CONS 837caRito religioso234880UNICAMPANIA01124nam0 22002893i 450 SUN010561120160617110406.23788--2-07-08-74-40.0020160526d1979 |0itac50 baitaIT|||| |||||*Forme e teorie contrattuali nell'età del principatoGeneroso MelilloNapoli : Liguori, 1979150 p.23 cmBiblioteca Lauria.Negozi giuridiciDiritto romanoSec. 1.-3.SGSUNC032499NapoliSUNL000005346.370221Melillo, GenerosoSUNV001815200830LiguoriSUNV000052650ITSOL20181109RICASUN0105611UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00 CONS BL.900M.698 00 BL 3844 SLP UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZABL3844CONS BL.900M.698 SLPcaForme e teorie contrattuali nell'età del principato664915UNICAMPANIA05695nam 2201273z- 450 991056648690332120220506(CKB)5680000000037510(oapen)https://directory.doabooks.org/handle/20.500.12854/81089(oapen)doab81089(EXLCZ)99568000000003751020202205d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEvolutionary Algorithms in Engineering Design OptimizationBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (314 p.)3-0365-2714-1 3-0365-2715-X Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.History of engineering & technologybicsscTechnology: general issuesbicsscaccuracy levelsaeroacousticsarchiving strategyartificial neural networks (ANN) limited training dataAutomatic Voltage Regulation systemavailabilitybankruptcy problembeam improvementsbeam T-junctions modelsChaotic optimizationclassificationcontroldesigndifferential evolutiondistance-baseddiversity controlencodingevolutionary algorithmevolutionary algorithmsevolutionary optimizationexperimental studyfinite elements analysisFractional Order Proportional-Integral-Derivative controllergenetic algorithmgenetic programmingglobal optimisationglobal optimizationGough-Stewartlaunchersmachine learningmachine visionmin-max optimizationmono and multi-objective optimizationmulti-objective decision-makingmulti-objective evolutionary algorithmsmulti-objective optimisationmulti-objective optimizationmutation-selectionnearly optimal solutionsneural networksnon-linear parametric identificationoptimal controloptimal designparallel manipulatorparameter optimizationPareto frontperformance metricsplastics thermoformingpreference in multi-objective optimizationpreventive maintenance schedulingquality controlreal applicationreusable launch vehiclerobustrobust designroughness measurementsheet thickness distributionspace systemsspaceplanessurrogateT-junctionstrailing-edge noisetrajectory optimisationtwo-stage methoduncertainty quantificationunscented transformationworst-case scenarioYellow Saddle Goatfish AlgorithmHistory of engineering & technologyTechnology: general issuesGreiner Davidedt1309575Gaspar‐Cunha AntónioedtHernández-Sosa DanieledtMinisci EdmondoedtZamuda AlešedtGreiner DavidothGaspar‐Cunha AntónioothHernández-Sosa DanielothMinisci EdmondoothZamuda AlešothBOOK9910566486903321Evolutionary Algorithms in Engineering Design Optimization3029408UNINA