Automated Design of Metaheuristics Using Reinforcement Learning Within a Nove…
Metaheuristic algorithms have been investigated intensively to address highly complex combinatorial optimization problems. However, most metaheuristic algorithms have been designed manually by rese…
A Multi-Transformation Evolutionary Framework for Influence Maximization in S…
Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluat…
Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning
Recently, many stochastic variance reduced alternating direction methods of multipliers (ADMMs) (e.g., SAG-ADMM and SVRG-ADMM) have made exciting progress such as linear convergence rate for strong…
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