Learning and Intelligent Optimization (eBook, PDF)

19th International Conference, LION 19, Prague, Czech Republic, June 15-19, 2025, Proceedings, Part II
Redaktion: Zhang, Yingqian; Moosaei, Hossein; Hladik, Milan

• Format: PDF

Produktbeschreibung

The two-volume set LNCS 15744 + 15745 constitutes the proceedings of the 19th International Conference on Learning and Intelligent Optimization, LION 2025, which was held in Prague, Czech Republic, during June 15-19, 2025.

The 40 full papers included in the proceedings were carefully reviewed and selected from 70 submissions. They focus on exploring the intersections of Artificial Intelligence, Machine Learning, and Operations Research.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 302
• Erscheinungstermin: 1. Januar 2026
• Englisch
• ISBN-13: 9783032091925
• Artikelnr.: 76270692

Inhaltsangabe

.
Autoregressive RL Approach for Mixed
Integer Linear Programs. .
Algorithm Configuration in the Unified Planning Framework. .
Learning to Repair Infeasible$^*$ Problems with Deep Reinforcement Learning on Graphs. .
Optimal Matched Block Design For Multi
Arm Experiments. .
CHORUS: Zero
shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code. .
Taxi re
positioning considering driver compliance. .
A Shared Memory Optimal Parallel Redistribution Algorithm for SMC Samplers with Variable Size Samples. .
A Hybrid Quantum
Inspired and Deep Learning Approach for the Capacitated Vehicle Routing Problem with Time Windows. .
Multi
Action Sampling with Deep Reinforcement Learning for Traveling Salesman Problem. .
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job
Shop Scheduling Problem. .
Codetector: A Framework for Zero
shot Detection of AI
Generated Code. .
Pushing the Limits of the Reactive Affine Shaker Algorithm to Higher Dimensions. .
Convex quadratic programming
based predictors: An algorithmic framework and a study of possibilities and computational challenges. .
Studies on a Bayesian Optimization Based Approach to Tune Hyperparameters of Matheuristics. .
Local iterative algorithms for approximate symmetry guided by network centralities. .
Addressing Over
fitting in Passive Constraint Acquisition through Active Learning. .
Learning to solve the Skill Vehicle Routing Problem with Deep Reinforcement Learning. .
CGD: Modifying the Loss Landscape by Gradient Regularization. .
Data Sampling
driven Adaptive Modification of Bus Routes Under Time
Varying Road Conditions.