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A Hybrid Multi-objective Evolutionary Algorithm for the Uncapacitated Exam Proximity Problem

  • Pascal Côté
  • Tony Wong
  • Robert Sabourin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3616)

Abstract

A hybrid Multi-Objective Evolutionary Algorithm is used to tackle the uncapacitated exam proximity problem. In this hybridization, local search operators are used instead of the traditional genetic recombination operators. One of the search operators is designed to repair unfeasible timetables produced by the initialization procedure and the mutation operator. The other search operator implements a simplified Variable Neighborhood Descent meta-heuristic and its role is to improve the proximity cost. The resulting non dominated timetables are compared with those produced by other optimization methods using 15 public domain datasets. Without special fine-tuning, the hybrid algorithm was able to produce timetables with good rankings in nine of the 15 datasets.

Keywords

Memetic Algorithm Constraint Violation Timetabling Problem Multiobjective Evolutionary Algorithm Variable Neighborhood Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pascal Côté
    • 1
  • Tony Wong
    • 1
  • Robert Sabourin
    • 1
  1. 1.Department of Automated Manufacturing EngineeringÉcole de technologie supérieure, Université du QuébecMontréalCanada

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