Fusion of Machine Learning and Metaheuristics for Practical Solutions
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 828
Special Issue Editor
Interests: swarm intelligence; optimization metaheuristics; machine learning; deep learning; computer vision; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hybrid methods of machine learning/deep learning and metaheuristics represent a promising research field with the potential for significant contributions across various domains. According to the no free lunch (NFL) theorem, there is no universal approach capable of solving all challenges; therefore, each machine learning and deep learning model must be tailored to the specific problem at hand. This non-deterministic polynomial hard (NP-hard) challenge is known in the literature as the hyper-parameter tuning problem, and it has been shown that metaheuristics can prevent this problem with great success. This proposed Special Issue invites practical applications of hybrid methods combining machine learning/deep learning and metaheuristics. While hyper-parameter optimization is the primary focus, contributions addressing other challenges, such as feature selection, neural network (NN) weight initialization, deep networks training, etc., are also highly encouraged. Applications of tuned models, such as various types of recurrent neural networks (RNNs) for time-series prediction and classification, generative adversarial networks (GANs) for generating synthetic datasets, you only look once (YOLO) architectures for object detection, convolutional neural networks (CNNs) for image classification, and combined CNN/RNN models with traditional machine learning models (extreme gradient boosting—XGBoost, Adaptive Boosting—AdaBoost, support vector machines—SVM, etc.) are highly desirable topics for this Special Issue. Additionally, applications from various fields are welcome, including the prediction of energy prices, energy production from renewable sources, the load forecasting of virtual machine instances in the cloud, the classification of medical images and disease identification, combining audio–visual analysis in the form of Mel spectrograms for the detection of respiratory diseases and vehicle speed using CNN deep models, as well as many others.
Dr. Nebojsa Bacanin
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- metaheuristics
- machine learning
- deep learning
- optimization
- hyper-parameter tuning
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