LEADER 01169nam--2200409---450- 001 990000770960203316 005 20050307112346.0 035 $a0077096 035 $aUSA010077096 035 $a(ALEPH)000077096USA01 035 $a0077096 100 $a20011126d1991----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aGeografia per l'ambiente$fUgo Leone 210 $aRoma$cLa nuova italia scientifica$d1991 215 $a173 p.$d24 cm 225 2 $aI manuali$v51 300 $aSegue: Appendice legislativa 410 $12001$aI manuali$v51 461 1$1001-------$12001 606 0 $aUomo e ambiente$yItalia 676 $a304.20945 700 1$aLEONE,$bUgo$011528 801 0$aIT$bsalbc$gISBD 912 $a990000770960203316 951 $aIII.1. 1677(I H 47)$b114071 LM$cI H 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20011126$lUSA01$h1345 979 $aPATTY$b90$c20011126$lUSA01$h1346 979 $c20020403$lUSA01$h1724 979 $aPATRY$b90$c20040406$lUSA01$h1653 979 $aCOPAT3$b90$c20050307$lUSA01$h1123 996 $aGeografia per l'ambiente$962154 997 $aUNISA LEADER 04287nam 22005775 450 001 9910984686703321 005 20250228120733.0 010 $a9789819635382 010 $a9819635381 024 7 $a10.1007/978-981-96-3538-2 035 $a(CKB)37726368400041 035 $a(MiAaPQ)EBC31927445 035 $a(Au-PeEL)EBL31927445 035 $a(DE-He213)978-981-96-3538-2 035 $a(OCoLC)1505735007 035 $a(EXLCZ)9937726368400041 100 $a20250228d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Multi-Criterion Optimization $e13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4?7, 2025, Proceedings, Part II /$fedited by Hemant Singh, Tapabrata Ray, Joshua Knowles, Xiaodong Li, Juergen Branke, Bing Wang, Akira Oyama 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (397 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15513 311 08$a9789819635375 311 08$a9819635373 327 $a -- Algorithm analysis. -- Visual Explanations of Some Problematic Search Behaviors of Frequently Used EMO Algorithms. -- Numerical Analysis of Pareto Set Modeling. -- When Is Non-deteriorating Population Update in MOEAs Beneficial?. -- Analysis of Merge Non-dominated Sorting Algorithm. -- Comparative Analysis of Indicators for Multi-objective Diversity Optimization. -- Performance Analysis of Constrained Evolutionary Multi-Objective Optimization Algorithms on Artificial and Real-World Problems. -- On the Approximation of the Entire Pareto Front of a Constrained Multi objective Optimization Problem. -- Small Population Size is Enough in Many Cases with External Archives. -- Surrogates and machine learning. -- Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations. -- Surrogate Strategies for Scalarisation-based Multi-objective Bayesian Optimizers. -- A Mixed-Fidelity Evaluation Algorithm for Efficient Constrained Multi- and Many-Objective Optimization: First Results. -- Efficient and Accurate Surrogate-Assisted Approach to Multi-Objective Optimization Using Deep Neural Networks. -- Large Language Model for Multiobjective Evolutionary Optimization. -- Multi-Objective Multi-Agent Reinforcement Learning for Autonomous Driving in Mixed-Traffic Environments. -- Parallel TD3 for Policy Gradient-based Multi-Condition Multi-Objective Optimisation. -- Multi-criteria decision support. -- Reliability-based MCDM Using Objective Preferences Under Variable Uncertainty. -- An Efficient Iterative Approach for Uniformly Representing Pareto Fronts. -- Preference Learning for Multi-objective Reinforcement Learning by Means of Supervised Learning. -- Bayesian preference elicitation for decision support in multi-objective optimization. 330 $aThis two-volume set LNCS 15512-15513 constitutes the proceedings of the 13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025, held in Canberra, ACT, Australia, in March 2025. The 38 full papers and 2 extended abstracts presented in this book were carefully reviewed and selected from 63 submissions. The papers are divided into the following topical sections: Part I : Algorithm design; Benchmarking; Applications. Part II : Algorithm analysis; Surrogates and machine learning; Multi-criteria decision support. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15513 606 $aArtificial intelligence 606 $aArtificial Intelligence 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.3 700 $aSingh$b Hemant$01790150 701 $aRay$b Tapabrata$01763711 701 $aKnowles$b Joshua$01790151 701 $aLi$b Xiaodong$01790152 701 $aBranke$b Juergen$01790153 701 $aWang$b Bing$01217580 701 $aOyama$b Akira$01790154 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910984686703321 996 $aEvolutionary Multi-Criterion Optimization$94326244 997 $aUNINA