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GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes
We design a directed multipartite graph encapsulating the solution space, called RGGR, where each node corresponds to a gene in the solution and the edge represents the relationship between adjacent nodes.
Generating meta-learning tasks to evolve parametric loss for classification learning
In this paper, we propose a meta-learning approach based on randomly generated meta-learning tasks to obtain a parametric loss for classification learning based on big data.
Mining the Weights Knowledge for Optimizing Neural Network Structures
We introduce a switcher neural network (SNN) that uses as inputs the weights of a task-specific neural network (called TNN for short).
Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks
We design a switcher neural network (SNN) to optimize the structure of the task neural network (TNN).
Evolving parametrized Loss for Image Classification Learning on Small Datasets
The MLN is evolved with the Evolutionary Strategy algorithm (ES) to an optimized loss function, such that a classifier, which optimized to minimize this loss, will achieve a good generalization effect.
