Learning and Intelligent Optimization : 19th International Conference, LION 19, Prague, Czech Republic, June 15-19, 2025, Proceedings, Part II (Lecture Notes in Computer Science)

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Learning and Intelligent Optimization : 19th International Conference, LION 19, Prague, Czech Republic, June 15-19, 2025, Proceedings, Part II (Lecture Notes in Computer Science)

Zhang, Yingqian (EDT)/ Hladik, Milan (EDT)/ Moosaei, Hossein (EDT)

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Springer Nature Switzerland AG（2025/11発売）
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ポイント 143pt

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製本 Paperback:紙装版/ペーパーバック版
言語 ENG
商品コード 9783032091918

Full Description

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.

.- 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.