Produktbild: Learning and Intelligent Optimization
Band 15745

Learning and Intelligent Optimization 19th International Conference, LION 19, Prague, Czech Republic, June 15–19, 2025, Proceedings, Part II

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.01.2026

Abbildungen

XII, 302 p. 78 illus., 71 illus. in color.

Herausgeber

Yingqian Zhang + weitere

Verlag

Springer

Seitenzahl

302

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

482 g

Sprache

Englisch

ISBN

978-3-032-09191-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.01.2026

Abbildungen

XII, 302 p. 78 illus., 71 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

302

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

482 g

Sprache

Englisch

ISBN

978-3-032-09191-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Learning and Intelligent Optimization

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    .- 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.