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Improving the performance of heuristic searches with judicious initial point selection

Tahaee, A ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/SEC.2008.65
  3. Publisher: 2008
  4. Abstract:
  5. In this paper we claim that local optimization can produce proper start point for genetic search. We completely test this claim on partitioning problem and on the performance of genetic search in a real problem that is finding aggregation tree in the sensor networks. The presented method (named Tendency algorithm) increases the performance of heuristic searches, and can be used in parallel with other tuning methods. The paper justifies the logic behind tendency algorithm by measuring the "entropy" of solution (in regard to optimal solution), and by numerous empirical tests. © 2008 IEEE
  6. Keywords:
  7. Genetic algorithms ; Heuristic algorithms ; Parallel algorithms ; Sensor networks ; Empirical tests ; Genetic searches ; Heuristic searches ; Local optimizations ; Optimal solutions ; Partitioning problems ; Point selections ; Real problems ; Tuning methods ; Heuristic methods
  8. Source: Proceedings of The 5th IEEE International Symposium on Embedded Computing, SEC 2008, 6 October 2008 through 8 October 2008, Beijing ; 2008 , Pages 14-19 ; 9780769533483 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4690717