Initialization strategies and diversity in evolutionary timetabling

  • Authors:

  • Edmund K. Burke;James P. Newall;Rupert F. Weare

  • Affiliations:

  • Automated Scheduling and Planning Group Department of Computer Science University of Nottingham University Park, Nottingham, UK NG7 2RD ekb@cs.nott.ac.uk;Automated Scheduling and Planning Group Department of Computer Science University of Nottingham University Park, Nottingham, UK NG7 2RD jpn@cs.nott.ac.uk;Cap Gemini UK plc 3rd Floor 51 Grey Street Newcastle Upon Tyne NE16EE UK

  • Venue:
  • Evolutionary Computation
  • Year:
  • 1998

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Abstract

This document seeks to provide a scientific basis by which different initialization algorithms for evolutionary timetabling may be compared. Seeding the initial population may be used to improve initial quality and provide a better starting point for the evolutionary algorithm. This must be tempered against the consideration that if the seeding algorithm produces very similar solutions, then the loss of genetic diversity may well lead to a worse final solution. Diversity, we hope, provides a good indication of how good the final solution will be, although only by running the evolutionary algorithm will the exact result be found. We will investigate the effects of heuristic seeding by taking quality and diversity measures of populations generated by heuristic initialization methods on both random and real-life data, as well as assessing the long-term performance of an evolutionary algorithm (found to work well on the timetabling problem) when using heuristic initialization. This will show how the use of heuristic initialization strategies can substantially improve the performance of evolutionary algorithms for the timetabling problem.