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Optimization and Learning 6th International Conference, OLA 2023, Malaga, Spain, May 3-5, 2023, Proceedings
Title
Optimization and Learning [electronic resource] : 6th International Conference, OLA 2023, Malaga, Spain, May 3-5, 2023, Proceedings / edited by Bernabé Dorronsoro, Francisco Chicano, Gregoire Danoy, El-Ghazali Talbi.
ISBN
9783031340208
Edition
1st ed. 2023.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Physical Description
1 online resource (XIV, 428 p.) 136 illus., 119 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book constitutes the refereed proceedings of the 6th International Conference on Optimization and Learning, OLA 2023, held in Malaga, Spain, during May 3-5, 2023. The 32 full papers included in this book were carefully reviewed and selected from 78 submissions. They were organized in topical sections as follows: advanced optimization; learning; learning methods to enhance optimization tools; optimization applied to learning methods; and real-world applications.
Variant and related titles
Springer ENIN.
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Format
Books / Online
Language
English
Added to Catalog
June 09, 2023
Series
Communications in Computer and Information Science, 1824
Contents
Advanced Optimization
A comparative study of fractal-based decomposition optimization
Diagonal Barzilai-Borwein rules in stochastic gradient-like methods
Algorithm Selection for Large-Scale Multi-objective Optimization
Solving a Multi-Objective Jop Shop Scheduling Problem With An Automatically Configured Evolutionary Algorithm
Solving the Nurse Scheduling Problem using the Whale Optimization Algorithm
A hierarchical Cooperative Coevolutionary approach to solve Very Large-scale Traveling Salesman Problem
Tornado: An Autonomous Chaotic Algorithm For High Dimensional Global Optimization Problems
Learning
Neural Network Information Leakage through Hidden Learning
Mixing Data Augmentation Methods for Semantic Segmentation
Real-time elastic partial shape matching using a neural network-based adjoint method
We won't get fooled again: when performance metric malfunction affects the landscape of hyperparameter optimization problems
Condition-based maintenance optimization under large action space with deep reinforcement learning method
Learning methods to enhance optimization tools
An Application of Machine Learning Tools to Predict the Number of Solutions for a Minimum Cardinality Set Covering Problem
Adaptative Local Search for a Pickup and Delivery Problem Applied to Large Parcel Distribution
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic
Multi-objective optimization of adhesive bonding process in constrained and noisy settings
Evaluating Surrogate Models for Robot Swarm Simulations
Interactive Job Scheduling with Partially Known Personnel Availabilities
Multi-Armed Bandit-based Metaheuristic Operator Selection: The Pendulum Algorithm binarization case
Optimization applied to learning methods
Binary Black Widow with Hill Climbing Algorithm for Feature Selection
Optimization of Fuzzy C-Means with Alternating Direction Method of Multipliers
Partial K-means with M outliers: Mathematical programs and complexity results
An optimization approach for optimizing PRIM's randomly generated rules using the Genetic Algorithm
Real-world Applications
A fast methodology to find Decisively Strong Association Rules (DSR)
Characterization and categorization of software programs on x86 architectures
Robot-Assisted Delivery problems and Their Exact Solutions
Modeling and analysis of organizational network analysis graphs based on employee data
Time Series Forecasting for Parking Occupancy: Case Study of Malaga and Birmingham Cities
E-scooters Routes Potential: Open Data Analysis in Current Infrastructure. Malaga Case
Automatic Generation of Subtitles for Videos of the Government of La Rioja
Estimation of the distribution of Body Mass Index (BMI) with sparse and low-quality data. The case of the Chilean adult population
A New Automated Customer Prioritization Method. .
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