Parallel Fast Simulated Annealing Algorithm for Linear ...

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FSA neighbor generation strategy 20 20 a 2 a 1 a 1 0 0 a 2 -20 -20 -20 -10 0 10 20 -20 -10 0 10 20 6
FSA temperature and termination criterion • Initial temperature T(0) is determined through a set of pilot runs consisting of k P positive transitions from the starting solution. It is supposed to guarantee predetermined initial level of worse solution’s acceptance probability P(0) resulting from the Metropolis rule. • Initial solution is obtained using feature selection algorithm introduced by Pal & Mitra in 2004. It is based on feature space clustering, with similar features forming distinctive clusters. As a measure of similarity maximal information compression index was defined. The partition itself is performed using k-nearest neighbor rule (here k=n − N is being used). • The termination criterion is either executing assumed number of iterations or fulfilling customized condition based on the estimator of the global minimum employing order statistics proposed recently for a class of stochastic random search algorithms by Bartkute and Sakalauskas (2009) 7
Page 1 and 2: 7th International Conference on Adv
Page 3 and 4: Curse of dimensionality - example S
Page 5: General description of the algorith
Page 9 and 10: Sample size reduction • For each
Page 11 and 12: Example: seeds dataset (7D→2D) Ou
Page 13 and 14: More details - cluster analysis gla
Page 15 and 16: Thank you for your attention!

Parallel Fast Simulated Annealing Algorithm for Linear ...

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