01026nam0-22003491i-450-99000377078040332120031110114139.00-12-703672-5000377078FED01000377078(Aleph)000377078FED0100037707820030910d--------km-y0itay50------baitay-------001yySocial DynamicsModels and MethodsNancy Brandon Tuma, Michael T. HannanOrlandoAcademic Press1984xx, 578 p.fig., tab.23 cmQuantitative studies in social relationsSociologiaRicercaMetodologiaScienze socialiRicercaMetodologia300.72301.072Tuma,Nancy Brandon143261Hannan,Michael T.ITUNINARICAUNIMARCBK990003770780403321300.72 TUM 170BFSBFSSocial Dynamics510866UNINA01057cam0-22003851i-450-99000029388040332120050622134808.0000029388FED01000029388(Aleph)000029388FED0100002938820020821d1963----km-y0itay50------baengUSa-------001yyDesign of equilibrium stage processeD. Budford SmithNew YorkMcGraw-Hill1963XI, 647 p.ill.23 cmMcGraw-Hill Chemical Engineering Series660541.392660.2Buford,Smith D.ITUNINARICAUNIMARCBK99000029388040332104 166-12CNR 416/LDINCH13 B 23 1929212FINBC13 B 42 1829871FINBC13 B 05 2029211FINBC04 166-13CI 6076DINCH04 166-22 ARIRC 659/LDINCHFINBCDINCHUNINA03393nam 2200793z- 450 991055762320332120210501(CKB)5400000000045183(oapen)https://directory.doabooks.org/handle/20.500.12854/69391(oapen)doab69391(EXLCZ)99540000000004518320202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEvolutionary Algorithms in Intelligent SystemsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (144 p.)3-03943-611-2 3-03943-612-0 Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.Information technology industriesbicsscadaptive local search operatorassociation rulesbig dataco-evolutionconstrained optimizationdifferential evolutionensemble of constraint handling techniquesevolutionary algorithmsformal methods in evolutionary algorithmsGaussian mutationglobal continuous optimizationhorizontal unionhybrid algorithmsimproved learning strategyinterval concept latticememetic particle swarm optimizationmining algorithmmulti-objective optimizationmulti-objective optimization problemsn/aneural networksneuroevolutionparameter analysisparameter puningparticle swarm optimizationparticle swarm optimization (PSO)PSOself-adaptive differential evolutionary algorithmssequence traversalsocial network optimizationstochastic optimizationtask allocationvertical unionwireless sensor networksInformation technology industriesMilani Alfredoedt1280355Carpi ArturoedtPoggioni ValentinaedtMilani AlfredoothCarpi ArturoothPoggioni ValentinaothBOOK9910557623203321Evolutionary Algorithms in Intelligent Systems3016818UNINA