The simplification function was introduced to PushGP as a tool to reduce the sizes of evolved programs in final reports. While previous work suggests that simplification could reduce the sizes significantly, nothing has been done to study its impacts on the evolution of Push programs. In this paper, we show the impact of simplification as a genetic operator. By conducting test runs on the U.S. change problem, we show that using simplification operator with PushGP, lexicase selection and ULTRA could increase the possibility to find solutions in the short term while it might remove some useful genetic materials for the long term.

Multi-view subspace clustering has conventionally focused on integrating heterogeneous feature descriptions to capture higher-dimensional information. One popular strategy is to generate a common subspace from different views and then apply graph-based approaches to deal with clustering. However, the performance of these methods is still subject to two limitations, namely the multiple views fusion pattern and the connection between the fusion process and clustering tasks.To address these problems, we propose a novel multi-view subspace clustering framework via finegrained graph learning, which can tell the consistency of local structures between different views and integrate all views more delicately than previous weight regularizations. Different from other models in the literature, the point-level graph regularization and the reformulation of spectral clustering are introduced to perform graphs fusion and learn the shared cluser structure together. Extensive experiments on five real-world datasets show that the proposed framework has comparable performance to the SOTA algorithms.

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