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Maximizing Search Efficiency through Common Component Coordination

Explore strategies for coordinating parallel searches with shared components to optimize search efficiency in globally convex spaces. Discussion includes search operators, control strategies, simulated annealing, and mutation strategies.

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Maximizing Search Efficiency through Common Component Coordination

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  1. The Coordination of Parallel Search with Common Components Stephen Chen and Gregory PittYork University

  2. Heuristic Search • Search Operators • Control Strategies IEA-AIE 2005

  3. An Obvious Failing • Point search operators do not have historical information! IEA-AIE 2005

  4. Search Operator Strategies • What can be improved? • What is worth keeping? IEA-AIE 2005

  5. Random Local Minima IEA-AIE 2005

  6. Simulated Annealing IEA-AIE 2005

  7. Summary • Random search space, random search strategy • All the intelligence of simulated annealing is in the control strategy IEA-AIE 2005

  8. Distribution of Local Minima (Boese) Length Average distance from other local minima IEA-AIE 2005

  9. “Big-Valley” – Globally Convex IEA-AIE 2005

  10. “Directed” Simulated Annealing IEA-AIE 2005

  11. Coordination with Common Components IEA-AIE 2005

  12. Restricted Mutations • Parent 1 1 1 1 0 0 1 0 1 1 1 • Parent 2 1 0 1 1 0 1 1 0 1 1 • Common 1 1 0 1 1 1 • Uncommon 1 1 0 0 1 • Uncommon 2 0 1 1 0 IEA-AIE 2005

  13. TSP Results IEA-AIE 2005

  14. Summary • Globally convex search space, “respectful” search operator • Add some intelligence to the search operator IEA-AIE 2005

  15. Conclusion • The preservation of common components can improve the performance of parallel search procedures in globally convex search spaces IEA-AIE 2005

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