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Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network by Yu, Shanqing; Zhou, Meng; Zhou, Jintao; Zhao, Minghao; Song, Yidan; Lu, Yao; Wang, Zeyu; Xuan, Qi is a scholarly article available to read on EtoBox.

What is Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network about?

Evolutionary computing, particularly genetic algorithm (GA), is a combinatorial optimization method inspired by natural selection and the transmission of genetic information, which is widely used to identify optimal solutions to complex problems through simulated programming and iteration. Due to its strong adaptability, flexibility, and robustness, GA has shown significant performance and potentiality on perturbed substructure optimization (PSSO), an important graph mining problem that achieves its goals by modifying network structures. However, the efficiency and practicality of GA-based PSSO face enormous challenges due to the complexity and diversity of application scenarios. While some research has explored acceleration frameworks in evolutionary computing, their performance on PSSO remains limited due to a lack of scenario generalizability. Based on these, this paper is the first to present the GA-based PSSO Acceleration framework (GAPA), which simplifies the GA development process and supports distributed acceleration. Specifically, it reconstructs the genetic operation and designs a development framework for efficient parallel acceleration. Meanwhile, GAPA includes an exten

Author

Yu, Shanqing; Zhou, Meng; Zhou, Jintao; Zhao, Minghao; Song, Yidan; Lu, Yao; Wang, Zeyu; Xuan, Qi

Published

2024

Language

EN