Computational Intelligence and Soft Computing: Recent Applications
The delicate balance of resource intensity and efficiency of a solution for a very complex problem raises the issue of how these aspects constitute a symmetrical or asymmetrical system. Computational Intelligence (CI) offers a plethora of efficient tools for the potential and rather good quality sol...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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700 | 1 | |a Harmati, István A. |4 edt | |
700 | 1 | |a László, Kóczy |4 oth | |
700 | 1 | |a Harmati, István A. |4 oth | |
245 | 1 | 0 | |a Computational Intelligence and Soft Computing: Recent Applications |
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520 | |a The delicate balance of resource intensity and efficiency of a solution for a very complex problem raises the issue of how these aspects constitute a symmetrical or asymmetrical system. Computational Intelligence (CI) offers a plethora of efficient tools for the potential and rather good quality solution of highly complex, often mathematically intractable problems. This "toolbox" is one of the best examples for the study of the above mentioned issue, namely, how to find the best balance, how to establish a symmetry of weights or costs in a particular field, for a particular problem. This special issue presents a number of interesting novel applications of CI for the tackling of a wide variety of difficult problems. The Introduction gives a short insight into the symmetry and asymmetry aspect of the topic. | ||
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653 | |a command and control | ||
653 | |a learning classifier system | ||
653 | |a multi-agent systems | ||
653 | |a weapon-target assignment | ||
653 | |a energy optimization | ||
653 | |a genetic algorithms | ||
653 | |a multi-objective optimization | ||
653 | |a artificial neural network simulator | ||
653 | |a artificial intelligence | ||
653 | |a artificial bee colony algorithm | ||
653 | |a global optimization | ||
653 | |a neural network | ||
653 | |a nonlinear static system | ||
653 | |a behavioural finance | ||
653 | |a imprecision | ||
653 | |a oriented fuzzy number | ||
653 | |a oriented present value | ||
653 | |a oriented return | ||
653 | |a S-divergence | ||
653 | |a S-distance | ||
653 | |a spectral clustering | ||
653 | |a text summarization | ||
653 | |a recurrent neural network | ||
653 | |a embedding | ||
653 | |a dynamic memory network | ||
653 | |a fuzzy-rough cognitive network | ||
653 | |a fuzzy cognitive map | ||
653 | |a granular computing | ||
653 | |a fuzzy-rough sets | ||
653 | |a stability | ||
653 | |a convergence | ||
653 | |a discrete bacterial memetic evolutionary algorithm | ||
653 | |a simulated annealing | ||
653 | |a flow shop scheduling problem | ||
653 | |a global sensitivity analysis | ||
653 | |a Sobol procedure | ||
653 | |a fast algorithm | ||
653 | |a convolutional neural network | ||
653 | |a structure reduction | ||
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653 | |a opinion mining | ||
653 | |a recurrent neural networks | ||
653 | |a sentiment classification | ||
653 | |a natural language processing | ||
653 | |a data augmentation | ||
653 | |a fine-tuning | ||
653 | |a generative models | ||
653 | |a StyleGAN | ||
653 | |a transfer learning | ||
653 | |a present value | ||
653 | |a discount factor | ||
653 | |a portfolio | ||
653 | |a finance | ||
653 | |a optimization | ||
653 | |a tourist trip design | ||
653 | |a vehicle routing | ||
653 | |a key performance indicator (KPI) | ||
653 | |a anomaly detection | ||
653 | |a variational auto-encoder (VAE) | ||
653 | |a support vector data description (SVDD) | ||
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856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/7134 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/100041 |7 0 |z DOAB: description of the publication |