Program for stream Data Science Meets Optimization

TA-05: Data Science and Optimization
Stream: Data Science Meets Optimization
Room: E (building Main lobby)
Chair(s): Ender Özcan, Andrew J. Parkes

• New hard 0-1 knapsack problem instances
  Jorik Jooken, Pieter Leyman, Patrick De Causmaecker
• Multiplicity in Signed Graph Partitioning
  Rosa Figueiredo, Nejat Arinik, Vincent Labatut
• Automated Algorithm Configuration for the Quadratic Unconstrained Binary Optimisation Problem
  Daniel Karapetyan, Jack Warren, Ender Özcan, Andrew J. Parkes
• Bi-objective Search for Acoustic Topology Optimisation and Noise Reduction
  Andrew J. Parkes, Vivek T. Ramamoorthy, Ender Özcan

TB-05: Integrating Machine Learning in Optimization Methods
Stream: Data Science Meets Optimization
Room: E (building Main lobby)
Chair(s): Michael Römer

• Learning to solve a stochastic orienteering problem with time windows
  André Hottung, Kevin Tierney
• Learning to Approximate State-Expanded Network Models
  Michael Römer
• State-based Reinforcement Learning for Hyper-heuristics
  Lucas Kletzander, Nysret Musliu
• Application of artificial neural networks in predicting cost of mechanical projects
  George Aretoulis, Christina Livitsanou

TC-05: Optimization Models for Machine Learning
Stream: Data Science Meets Optimization
Room: E (building Main lobby)
Chair(s): Dimitri Papadimitriou

• Rolling Look-Ahead Approaches for Optimal Classification Trees
  Zeynel Batuhan Organ, Enis Kayis, Taghi Khaniyev
• A Novel Optimization Based Hyperbox Approach for Multi-Class Data Classification Problem
  Fatih Rahim
• Photovoltaic self-consumption optimization for Home Microgrid: A Deep Reinforcement Learning approach
  Mohamed Saâd El Harrab, Michel Nakhla
• Using industrial data to enhance a solution approach for a multi-objective real-time railway rescheduling problem
  Hugo Belhomme, Stéphane DAUZERE-PERES, Mathieu Gagnon, François Ramond

TD-05: Better Decisions with Data
Stream: Data Science Meets Optimization
Room: E (building Main lobby)
Chair(s): Patrick De Causmaecker

• Investigating parallel implementations of genetic algorithms in stochastic part-of-speech tagging
  Shimanto Rahman
• Product range optimization using geo-spatial data
  Ondřej Sokol
• A Reinforcement Learning and Monte Carlo Tree Search Approach for Data-Driven Supply Chain Management
  Felipe Maldonado, Florian Antoni
• A PARALLEL NEURAL NETWORK MODEL TO PREDICT MOLECULAR PARAMETERS FROM ASTRONOMICAL EMISSION LINES WITH LINEAR SPEEDUP
  Mauricio Solar

WA-05: Automated algorithm tuning, configuration and construction
Stream: Data Science Meets Optimization
Room: E (building Main lobby)
Chair(s): Manuel López-Ibáñez

• Selector: An ensemble for automated algorithm configuration
  Elias Schede, Dimitri Weiß, Kevin Tierney
• Realtime Gray-Box Algorithm Configuration
  Dimitri Weiß
• A Real-world Case Study of Automatic Selection of GRASP Configurations with Single-selectors vs Multi-Selectors
  Manuel López-Ibáñez, Nicolás Álvarez, Silvino Fernández Alzueta, Pablo Valledor Pellicer
• The Hurst parameter: a way of finding statistical significant differences between healthy subjects and congestive heart failure patients
  MAR FENOY