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We propose Spectral Feature Alignment (SFA) [32] based algorithm to find a new representation for cross-domain process data, such that the gap between domains can be reduced.
In [ 3], Erten, Bebek and Koyuturk investigate the topological similarity in PPI networks and suggest a random walk based algorithm to find genes with similar disease.
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We also propose R-tree based algorithms to find the influence set for a given query point and multiple feature types.
If the suitable relay node does not exist, then we will use the second algorithm which is a probe-based algorithm to find out the relay cluster head node.
As NSGA-II is recognized to be well qualified to tackle MOPs, we then propose a NSGA-II-based algorithm to find the best node distribution in each corona.
We use a bipartite graph to represent the RA-CORP problem, and then use a sparse matrix-based algorithm to find the maximum-sized matching between the MNs and RNs.
Typically, they start with a constraint-based algorithm to find the skeleton of the network and then employ a score-based method to identify the best set of edge orientations.
In this paper, we have applied the concept of training and testing with a self-adaptive multi-operator based evolutionary algorithm to find suitable parameters.
Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition.
We also develop a heuristic algorithm based on Genetic Algorithm, to find the possible optimal intervention strategy for networks of large size.
They also developed a heuristic algorithm based on Genetic Algorithm, to find the optimal intervention strategy for networks with large sizes.
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