01539nam0-2200433---450-99000226135020331620091217090644.088-14-11110-3000226135USA01000226135(ALEPH)000226135USA0100022613520041213d2004----km-y0itay0103----baitaIT||||||||001yy<<I>> nuovi danni non patrimonialiil risarcimento dei pregiudizi non pecuniati dopo Cass. 8827/03Rodolfo Berti, Flavio Peccenici, Marco RossettiMilanoGiuffrè2004XIII, 211 p.21 cmTeoria e pratica del dirittoSez. 1Diritto e procedura civile1102001Teoria e pratica del dirittoSez. 1Diritto e procedura civile110Danni moraliRisarcimentoLegislazione345.45033BERTI,Rodolfo570188PECCENINI,Flavio234447ROSSETTI,Marco151043ITsalbcISBD990002261350203316XXV.1. Coll. 15/ 120 (COLL ZJ/I 110)43852 G.XXV.1. Coll. 15/ 120 (COLL ZJ)00139753COLL XXIII/a 1104592 DIRCEBKGIUDIRCEJOHNNY9020041213USA011211MARIASEN9020050223USA011725DIRCE9020051108USA011041RSIAV49020091217USA010906Nuovi danni non patrimoniali1066544UNISA03440nam 22005175 450 991029994040332120200630172808.03-319-70851-110.1007/978-3-319-70851-5(CKB)4100000002892106(MiAaPQ)EBC5358048(DE-He213)978-3-319-70851-5(PPN)225551500(EXLCZ)99410000000289210620180314d2018 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic /by Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (110 pages)SpringerBriefs in Computational Intelligence,2625-37043-319-70850-3 Introduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results.In this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the optimization method, because with the modification of parameters, these will affect directly the way in which the global or local search are performed. Three different optimization methods were used to verify the improve-ment of the proposed methodology. In this case the optimization methods are: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), where some parameters are se-lected to be dynamically adjusted, and these parameters have the most im-pact in the behavior of each optimization method. Simulation results show that the proposed methodology helps to each optimization method in obtaining better results than the results obtained by the original method without parameter adjustment.SpringerBriefs in Computational Intelligence,2625-3704Computational intelligenceArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.006.3Olivas Frumenauthttp://id.loc.gov/vocabulary/relators/aut1063586Valdez Fevrierauthttp://id.loc.gov/vocabulary/relators/autCastillo Oscarauthttp://id.loc.gov/vocabulary/relators/autMelin Patriciaauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299940403321Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic2533065UNINA