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PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Our proposed method creates algorithm behavior meta-representations, constructs a graph from a set of algorithms based on their meta-representation similarity, and applies a graph algorithm to select a final portfolio of diverse, representative, and non-redundant algorithms.
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes
Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP.
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