Trap Avoidance in Local Search Using Pseudo-Conflict Learning
Author(s)
Pham, DN
Duong, TT
Sattar, A
Griffith University Author(s)
Year published
2012
Metadata
Show full item recordAbstract
A key challenge in developing efficient local search solvers is to effectively minimise search stagnation (i.e. avoiding traps or local minima). A majority of the state-of-the-art local search solvers perform random and/or Novelty-based walks to overcome search stagnation. Although such strategies are effective in diversifying a search from its current local minimum, they do not actively prevent the search from visiting previously encountered local minima. In this paper, we propose a new preventative strategy to effectively minimise search stagnation using pseudo-conflict learning. We define a pseudo-conflict as a ...
View more >A key challenge in developing efficient local search solvers is to effectively minimise search stagnation (i.e. avoiding traps or local minima). A majority of the state-of-the-art local search solvers perform random and/or Novelty-based walks to overcome search stagnation. Although such strategies are effective in diversifying a search from its current local minimum, they do not actively prevent the search from visiting previously encountered local minima. In this paper, we propose a new preventative strategy to effectively minimise search stagnation using pseudo-conflict learning. We define a pseudo-conflict as a derived path from the search trajectory that leads to a local minimum. We then introduce a new variable selection scheme that penalises variables causing those pseudo-conflicts. Our experimental results show that the new preventative approach significantly improves the performance of local search solvers on a wide range of structured and random benchmarks.
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View more >A key challenge in developing efficient local search solvers is to effectively minimise search stagnation (i.e. avoiding traps or local minima). A majority of the state-of-the-art local search solvers perform random and/or Novelty-based walks to overcome search stagnation. Although such strategies are effective in diversifying a search from its current local minimum, they do not actively prevent the search from visiting previously encountered local minima. In this paper, we propose a new preventative strategy to effectively minimise search stagnation using pseudo-conflict learning. We define a pseudo-conflict as a derived path from the search trajectory that leads to a local minimum. We then introduce a new variable selection scheme that penalises variables causing those pseudo-conflicts. Our experimental results show that the new preventative approach significantly improves the performance of local search solvers on a wide range of structured and random benchmarks.
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Conference Title
Proceedings of the National Conference on Artificial Intelligence
Volume
1
Publisher URI
Subject
Artificial intelligence not elsewhere classified