Automated/Evolutionary Deep Learning and Applications to Image Classification

Image classification problems occur in our everyday life. Recognising faces in digital images and diagnosing medical conditions from X-Ray images are just two examples of the many important tasks for which we need computer based image classification systems. Since the 1980s, many image analysis algorithms have been developed. Among those algorithms, deep learning particularly deep convolutional neural networks have received very good success and attracted attentions to industry people and researchers in computer vision and image processing, neural networks, and machine learning. However, there are at least three major limitations in deep convolutional neural networks:  (1) the learning architecture including the number of layers, the number of feature maps in each layer and the number of nodes in each feature map are still very much determined manually via “trial and error”, which requires a large amount of hand-crafting/trial time and good domain knowledge. However, such experts are hard to find in many cases, or using such expertise is too expensive.  (2) Almost all the current deep learning algorithms need a large number of examples/instances (e.g. AlphaGo used over 30 million instances) that many problems do not have. (3) Those algorithms require a huge computational cost that big companies such as Google, Baidu, and Microsoft can cope well but most universities and research institutions cannot. To address these limitations, evolutionary computation techniques start playing a significant role for automatically determining deep structures, transfer functions and parameters to tackle image classification tasks, and have great potential to advance the developments of deep structures and algorithms. This talk will provide an extended view of deep learning, overview the state-of-the-art work in evolutionary deep learning using Genetic Algorithms (GAs), Particle Swarm Optimisation (PSO) and Differential Evolution (DE), and discuss some recent developments using Genetic Programming (GP) to automatically evolving deep structures and feature construction for image recognition with a highlight of the interpretation capability and visualisation of constructed features. If time allows, the talk will discuss GP applications to biomarker detection and peptide detection.

Prof. Dr. Mengjie Zhang

Victory University of Wellington

CIS Distinguished Lecturer

Sponsored by the Computational Intelligence Society under its Distinguished Lecturers Program.
Time: Sep. 29th, 2021 – 7 pm (Rio de Janeiro time).
Location: Online event via Zoom.
Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, an IEEE Distinguished Lecturer, currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science.
His research is mainly focused on artificial intelligence (AI), machine learning and big data, particularly in evolutionary computation and learning (using genetic programming, particle swarm optimisation and learning classifier systems), feature selection/construction and big dimensionality reduction, computer vision and image processing, job shop scheduling and resource allocation, multi-objective optimisation,  classification with unbalanced data and missing data, and evolutionary deep learning and transfer learning. Prof Zhang has published over 600 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over ten international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Emergent Topics in Computational Intelligence, ACM Transactions on Evolutionary Learning and Optimisation, the Evolutionary Computation Journal (MIT Press), Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, Natural Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been involving major AI and EC conferences such as GECCO, IEEE CEC, EvoStar, AAAI, PRICAI, PAKDD, AusAI, IEEE SSCI and SEAL as a Chair. He has also been serving as a steering committee member and a program committee member for over 100 international conferences. Since 2007, he has been listed as one of the top ten (currently No. 4) world genetic programming researchers by the GP bibliography (
Prof Zhang is the chair of the IEEE CIS Outstanding PhD Dissertation Award sub committee, the chair of the PubsCom strategic planning sub Committee, and a past Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee and the IEEE CIS Evolutionary Computation Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
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