Zhang / Hladik / Moosaei Learning and Intelligent Optimization

19th International Conference, LION 19, Prague, Czech Republic, June 15–19, 2025, Proceedings, Part II
Erscheinungsjahr 2026
ISBN: 978-3-032-09192-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 302 Seiten

Reihe: Springer Nature Proceedings Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Yingqian

Hladik, Milan

Moosaei, Hossein

Inhaltsverzeichnis

.- 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.