Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics II

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 456

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Special Issue Information

Dear Colleagues,

Machine learning (ML) has been widely applied for big data processing and analytics, where various optimization problems (regarding model symmetry/asymmetry, model architecture and hyperparameters, data clustering, and data prediction) are frequently encountered. The automatic design of machine learning has become an increasingly popular research trend. Evolutionary computation (EC) is commonly used in these scenarios, where classical numerical methods fail to find good enough solutions. Evolutionary approaches can be used in all the parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting and network topology), and postprocessing (e.g., decision tree/support vector pruning and ensemble learning). It is of great interest to investigate the combination of EC and ML in solving large-scale big data analytical problems.

The interdisciplinary research of this topic focuses on the progress of machine learning and evolutionary algorithms and their applications for big data, as well as emerging intelligent applications and models in topics of interest, including, but not limited to, industrial control, job-shop scheduling, expert systems, pattern recognition, and computer vision.

This Special Issue aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary machine learning and big data, in particular, the integration between academic research and industry applications, and to stimulate further engagement with the user community. With this Special Issue, we aim to disseminate knowledge among researchers, designers, and users in this exciting field.

This Special Issue is the second edition of the Special Issue “Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics” (https://www.mdpi.com/journal/symmetry/special_issues/Symmetric_Machine_Learning)

Prof. Dr. Shangce Gao
Prof. Dr. Lianbo Ma
Guest Editors

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Keywords

  • symmetry
  • machine learning
  • evolutionary computation
  • multi-objective optimization
  • big data processing
  • deep learning models
  • neural architecture search
  • intelligent systems
  • industrial applications

Published Papers (1 paper)

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13 pages, 442 KiB  
Article
Knowledge-Guided Parallel Hybrid Local Search Algorithm for Solving Time-Dependent Agile Satellite Scheduling Problems
by Yuyuan Shan, Xueping Wang, Shi Cheng, Mingming Zhang and Lining Xing
Symmetry 2024, 16(7), 813; https://doi.org/10.3390/sym16070813 - 28 Jun 2024
Viewed by 325
Abstract
As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving [...] Read more.
As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes. Full article
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