{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":43,"n_cols":5,"rows_kept":43,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[10,2,10,3],[11,2,11,3],[12,2,12,3],[14,2,14,3],[14,4,14,5],[15,2,15,3],[15,4,15,5],[16,2,16,3],[17,2,17,3],[19,2,19,3],[20,2,20,3],[21,2,21,3],[22,2,22,3],[23,2,23,3],[35,2,35,3],[36,2,36,3],[39,2,39,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA990002671990203316"]
[null,null,"Autore",null,"CASOTTI, Alfredo"]
[null,null,"Titolo",null,"Le strategie difensive del datore di lavoro : D.Lgs. 124/2004 : dalla diffida accertativa alla conciliazione monocratica / Alfredo Casotti, Maria Rosa Gheido"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Giuffrè, copyr. 2005"]
[null,null,"ISBN",null,"88-14-11885-X"]
[null,null,"Descrizione fisica",null,"X, 194 p. ; 24 cm"]
[null,null,"Collana",null,"Cosa & come , Lavoro e previdenza"]
[null,null,"Disciplina",null,"344.4501"]
[null,null,"Soggetti",null,"Ispettorato del lavoro - Legislazione"]
[{"$nulls":4},"Controversie di lavoro"]
[null,null,"Collocazione",null,"XXV.2.C 15 (IRA 40 244)"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910682588503321"]
[null,null,"Titolo",null,"Evolutionary Multi-Criterion Optimization : 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings / / edited by Michael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"9783031272509"]
[{"$nulls":4},"3031272501"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (646 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 13970"]
[null,null,"Disciplina",null,"519.3"]
[null,null,"Soggetti",null,"Algorithms"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Design and Analysis of Algorithms"]
[{"$nulls":4},"Mathematics of Computing"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computing Milieux"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Algorithm Design and Engineering -- Visual Exploration of the Effect of Constraint Handling in Multiobjective Optimization -- A Two-stage Algorithm for Integer Multiobjective Simulation Optimization -- RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving -- Cooperative coevolutionary NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective Optimization -- Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts -- Eliminating Non-dominated Sorting from NSGA-III -- Scalability of Multi-Objective Evolutionary Algorithms for Solving Real-World "]
[{"$nulls":4},"Complex Optimization Problems -- Machine Learning and Multi-criterion Optimization -- Multi-Objective Learning using HV Maximization -- Sparse Adversarial Attack via Bi-Objective Optimization -- Investigating Innovized Progress Operators with Different Machine Learning Methods -- End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location -- Online Learning Hyper-Heuristics in Multi-Objective Evolutionary Algorithms -- Surrogate-assisted Multi-objective Optimization via Genetic Programming based Symbolic Regression -- Learning to Predict Pareto-optimal Solutions From Pseudo-weights -- A Relation Surrogate Model for Expensive Multiobjective Continuous and Combinatorial Optimization -- Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling -- Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling -- Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables -- Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective -- Benchmarking and Performance Assessment -- Partially Degenerate Multi-Objective Test Problems -- Peak-A-Boo! GeneratingMulti-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets -- MACO: A Real-world inspired Benchmark for Multi-objective Evolutionary Algorithms -- A scalable test suite for bi-objective multidisciplinary optimisation -- Performance Evaluation of Multi-Objective Evolutionary Algorithms using Artificial and Real-World Problems -- A Novel Performance Indicator based on the Linear Assignment Problem -- A Test Suite for Multi-objective Multi-fidelity Optimization -- Indicator Design and Complexity Analysis -- Diversity enhancement via magnitude -- Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems -- Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems -- On the Computational Complexity of Efficient Non-Dominated Sort using Binary Search -- Applications in Real World Domains -- Evolutionary Algorithms with Machine Learning Models for Multiobjective Optimization in Epidemics Control -- Joint Price Optimization across a Portfolio of Fashion E-commerce Products -- Improving MOEA/D with Knowledge Discovery. Application to a Bi-Objective Routing Problem -- The Prism-Net Search Space Representation for Multi-Objective Building Spatial Design -- Selection Strategies for a Balanced Multi- or Many-Objective Molecular Optimization and Genetic Diversity: a Comparative Study -- A Multi-objective Evolutionary Framework for Identifying Dengue Stage-Specific Differentially Co-expressed and Functionally Enriched Gene Modules -- A Multi-objective Evolutionary Framework for Identifying Dengue Stage-Specific Differentially Co-expressed and Functionally Enriched Gene Modules. -Multiobjective Optimization of Evolutionary Neural Networks for Animal Trade Movements Prediction -- Transfer of Multi-Objectively Tuned CMA-ES Parameters to a Vehicle Dynamics Problem -- Multi-Criteria Decision Making and Interactive Algorithms -- Preference-Based Nonlinear Normalization for Multiobjective Optimization -- Incorporating preference information interactively in NSGA-III by the adaptation of reference vectors -- A Systematic Way of Structuring Real-World Multiobjective Optimization Problems -- IK-EMOViz: An Interactive Knowledge-based Evolutionary Multi-objective Optimization Framework -- An Interactive Decision Tree-Based Evolutionary Multi-Objective Algorithm."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2022 held in Leiden, The Netherlands, during March 20-24, 2023. "]
[{"$nulls":4},"The 44 regular papers presented in this book were carefully reviewed and selected from 65 submissions. The papers are divided into the following topical sections: Algorithm Design and Engineering; Machine Learning and Multi-criterion Optimization; Benchmarking and Performance Assessment; Indicator Design and Complexity Analysis; Applications in Real World Domains; and Multi-Criteria Decision Making and Interactive Algorithms."]