A Comparison of PCA and GA Selected Features for Cloud Field Classification
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Optimization study with ligand-design interval rules
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Evolving neural networks for static single-position automated trading
Journal of Artificial Evolution and Applications - Regular issue
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
Balancing Parent and Offspring Selection in Genetic Programming
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
Performance analysis of distributed video-on-demand (VoD) systems
International Journal of Information and Communication Technology
An overview of AI research in Italy
Artificial intelligence
ICA and GA feature extraction and selection for cloud classification
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Hybrid genetic algorithm for minimum dominating set problem
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
Software evolution and natural processes: a taxonomy of approaches
ISIICT'09 Proceedings of the Third international conference on Innovation and Information and Communication Technology
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Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergistically.