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ML: Performance Measures
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Classroom Contents
Guiding Metaheuristics Through Machine Learning Predictions for Dynamic Autonomous Ridesharing
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- 1 Guiding metaheuristics trough machine learning predictions
- 2 Urban Mobility and Logistics
- 3 The Dial-a-Ride Problem (DARP)¹
- 4 The Electric Autonomous Dial-a-Ride Problem (e-ADARP)2
- 5 The (Dynamic) e-ADARP
- 6 Two-Phase Metaheuristic
- 7 Popular Operators and Metaheuristics
- 8 Machine Learning-Based Large Neighborhood Search
- 9 The Uber Dataset?
- 10 Event-Based Simulation Framework
- 11 Creating Examples (Labeled Dataset)
- 12 Statistics Example
- 13 Extracted Features
- 14 The Prediction Problem
- 15 The MLNS Algorithm
- 16 ML: Training Phase
- 17 ML: Performance Measures
- 18 ML: Features Importance
- 19 Optimization: Validation Phase
- 20 Expected difference in the objective function improvemen
- 21 Summary of Contributions
