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OL2A 2024 : 4th International Conference on Optimization, Learning Algorithms and Applications

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Link: https://ol2a.ipb.pt/
 
When Jul 24, 2024 - Jul 26, 2024
Where Tenerife, Spain
Submission Deadline Apr 15, 2024
Notification Due May 31, 2024
Final Version Due Jun 10, 2024
Categories    optimization   machine learning   stochastic programming   multi-objective optimization
 

Call For Papers

The 4th edition of the International Conference on Optimization, Learning Algorithms and Applications (OL2A 2024) will provide a forum where academic scientists, researchers and research scholars will have the opportunity to exchange and share their experiences and research results on the challenges of optimization and learning methods and their applications.

OL2A 2024 offers the international research community in optimization and learning an opportunity to present and debate the most recent results, trends and concerns as well as practical challenges that have been faced and solutions adopted in all fields of optimization and learning, as well as new high-impact applications such as 4th industrial revolution, multi-objective optimization, optimization for machine learning, machine learning for optimization, computer vision, machine learning speech recognition, natural language processing, machine learning for predictive analytics, machine learning for data analytics, and others.

Participants can attend the conference on-site, at University of La Laguna (San Cristóbal de La Laguna - Tenerife, Spain), or online (all OL2A sessions will be streamed live).

All submitted papers will pass through a multiple peer-review process and will carefully be evaluated based on originality, significance, technical soundness, and clarity of exposition. Conference proceedings will be published in SCOPUS indexed Springer-Verlag Book Series, Communications in Computer and Information Science (CCIS).

Topics

Considering the two goals of the OL2A Conference – optimization and learning algorithms – the topics are the following (but not limited to)

Learning theory
Optimization theory
Linear optimization algorithms
Nonlinear optimization algorithms
Population-based algorithms
Evolutionary-based algorithms
Metaheuristic optimization
Multi-objective Optimization
Mixed-integer optimization
Integer programing
Semi-infinite programming
Dynamic programming
Stochastic programming
Uncertain programming
Optimization in control systems
Big data and AI
Learning algorithms
Machine learning and soft computing
Deep learning algorithms
Clustering and classification algorithms
Prediction algorithms
Prediction mathematical models
Engineering (or other) applications using learning or optimization methods
Engineering education using learning or optimization methods

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