Intelligent Computing and Optimization
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2726
Special Issue Editors
Interests: automl; self-adjusting algorithms; cluster analysis; facility location; pseudo-boolean optimization; evolutionary computation
Interests: numerical linear algebra; operations research; nonlinear optimization; heuristic optimization; hybrid methods of optimization; gradient neural networks; zeroing neural networks; symbolic computation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Intelligent computing has greatly expanded the scope of computing, extending it from traditional computing on data to increasingly heterogeneous computing paradigms. At the same time, the majority of machine-learning methods are based on optimization theory and optimization algorithms, minimizing the intercluster distances or maximizing the Rand index in the cluster analysis, minimizing error in the regression, minimizing error rate in classification problems, etc. Optimization problems arising in machine learning are often large-scale and multimodal, and the efficiency of specific optimization algorithms strongly depends on the data set. These difficulties require special computation techniques such as parallelizing, hardware implementation of the computation, self-configuring capabilities of the algorithms, and the hybridization of different and diverse approaches: mathematically provable/heuristic methods, discrete/continuous optimization, etc. Self-configuration requires optimizing the efficiency of the algorithm, which solves the machine-learning or other optimization problem. Moreover, the necessity to increase the efficiency of optimization algorithms can lead to the use of machine-learning algorithms embedded into optimization algorithms.
This Special Issue aims to be a platform to share the recent advances in topics such as (non-exhaustive list):
- AutoML, self-configuring, adaptive and self-adjusting methods for optimization and machine learning;
- Methods of optimization and machine learning intended for hardware implementation;
- Mathematical optimization;
- Multiobjective optimization;
- Evolutionary computation;
- Fuzzy systems;
- Parallel algorithms for optimization and machine learning;
- Hybrid optimization algorithms and algorithmic combinations;
- Optimizing artificial neural networks;
- Optimization software and decision support systems;
- Applications of intelligent computing.
Prof. Dr. Lev Kazakovtsev
Prof. Dr. Predrag S. Stanimirovic
Guest Editors
Manuscript Submission Information
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Keywords
- intelligent computing
- optimization
- AutoML
- self-configuring optimization algorithms
- evolutionary computation
- hybrid optimization
- gradient neural networks