Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization

  • Ioannis T. Christou
  • Wayne Martin
  • Robert R. Meyer
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 111)


We present high-level, decomposition-based algorithms for large-scale block-angular optimization problems containing integer variables, and demonstrate their effectiveness in the solution of large-scale graph partitioning problems. These algorithms combine the subproblem-coordination paradigm (and lower bounds) of price-directive decomposition methods with knapsack and genetic approaches to the utilization of “building blocks” of partial solutions. Even for graph partitioning problems requiring billions of variables in a standard 0–1 formulation, this approach produces high-quality solutions (as measured by deviations from an easily computed lower bound), and substantially outperforms widely-used graph partitioning techniques based on heuristics and spectral methods.


Genetic Algorithm Optimal Shape Knapsack Problem Rectangular Domain Quadratic Assignment Problem 
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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Ioannis T. Christou
    • 1
  • Wayne Martin
    • 1
  • Robert R. Meyer
    • 1
  1. 1.Computer Sciences DepartmentUniversity of WisconsinMadisonUSA

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