A. K. Qin mostly deals with Artificial intelligence, Mathematical optimization, Evolutionary algorithm, Optimization problem and Meta-optimization. His Artificial intelligence research includes themes of Relevance and Pattern recognition. He focuses mostly in the field of Mathematical optimization, narrowing it down to matters related to Benchmark and, in some cases, Field.
His Evolutionary algorithm research incorporates themes from Artificial neural network, Stability, Extreme learning machine and Generalization error. His research integrates issues of Multi-objective optimization and Stochastic programming in his study of Optimization problem. His Multi-swarm optimization research incorporates elements of Swarm intelligence, Swarm behaviour and Premature convergence.
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Mathematical optimization and Evolutionary algorithm. His work on Feature extraction, Artificial neural network and Deep learning is typically connected to Process as part of general Artificial intelligence study, connecting several disciplines of science. His studies in Differential evolution, Evolutionary computation, Optimization problem, Multi-swarm optimization and Metaheuristic are all subfields of Mathematical optimization research.
The Evolutionary computation study combines topics in areas such as Field, Continuous optimization, Meta-optimization and Test functions for optimization. In his work, Premature convergence and Swarm intelligence is strongly intertwined with Swarm behaviour, which is a subfield of Multi-swarm optimization. The concepts of his Evolutionary algorithm study are interwoven with issues in Curse of dimensionality, Heuristic and Generalization error.
A. K. Qin mainly investigates Artificial intelligence, Machine learning, Feature extraction, Optimization problem and Pattern recognition. His Deep learning, Image and Feature study in the realm of Artificial intelligence interacts with subjects such as Process. His Feature extraction research includes elements of Artificial neural network, Algorithm, Search algorithm and Function.
His research in Optimization problem intersects with topics in Evolutionary algorithm and Evolutionary computation. His Evolutionary algorithm study results in a more complete grasp of Mathematical optimization. His Evolutionary computation study integrates concerns from other disciplines, such as Field and Swarm behaviour.
His primary scientific interests are in Artificial intelligence, Artificial neural network, Lens, Redshift and Convolutional neural network. His Artificial intelligence study incorporates themes from Machine learning and Differential privacy. When carried out as part of a general Machine learning research project, his work on Evolutionary computation is frequently linked to work in Human multitasking and Task analysis, therefore connecting diverse disciplines of study.
The study incorporates disciplines such as Evolutionary algorithm and Autoencoder in addition to Evolutionary computation. His study in Artificial neural network is interdisciplinary in nature, drawing from both Structure, Statistical model and Pattern recognition. His Optimization problem study is related to the wider topic of Mathematical optimization.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
J.J. Liang;A.K. Qin;P.N. Suganthan;S. Baskar.
IEEE Transactions on Evolutionary Computation (2006)
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
A.K. Qin;V.L. Huang;P.N. Suganthan.
IEEE Transactions on Evolutionary Computation (2009)
Self-adaptive differential evolution algorithm for numerical optimization
A.K. Qin;P.N. Suganthan.
congress on evolutionary computation (2005)
Rapid and brief communication: Evolutionary extreme learning machine
Qin-Yu Zhu;A. K. Qin;P. N. Suganthan;Guang-Bin Huang.
Pattern Recognition (2005)
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
Chong Zhang;Pin Lim;A. K. Qin;Kay Chen Tan.
IEEE Transactions on Neural Networks (2017)
Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization
V.L. Huang;A.K. Qin;P.N. Suganthan.
ieee international conference on evolutionary computation (2006)
Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty
P. Yu;A. K. Qin;D. A. Clausi.
IEEE Transactions on Geoscience and Remote Sensing (2012)
Robust growing neural gas algorithm with application in cluster analysis
A. K. Qin;P. N. Suganthan.
Neural Networks (2004)
A review of population initialization techniques for evolutionary algorithms
Borhan Kazimipour;Xiaodong Li;A. K. Qin.
congress on evolutionary computation (2014)
Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty
A K Qin;David A Clausi.
IEEE Transactions on Image Processing (2010)
Profile was last updated on December 6th, 2021.
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