883 citations · 2.5k across the 44 of their papers we have counts for
44 papers
HPSO: Particle Swarm Optimization with Hypergraph-Based Topology
Wenbin Pei, Xi Luo, Bing Xue +2
Particle swarm optimization (PSO) has been widely applied to solve complex optimization problems from real-world applications due to its efficient exploration of large solution spa…
Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning
Christian Raymond, Qi Chen, Bing Xue +1
In this paper, we develop upon the topic of loss function learning, an emergent meta-learning paradigm that aims to learn loss functions that significantly improve the performance…
Differentiable Genetic Programming for High-dimensional Symbolic Regression
Peng Zeng, Xiaotian Song, Andrew Lensen +4
Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable mac…
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
Bin Wang, Wenbin Pei, Bing Xue +1
Deep convolutional neural networks have proven their effectiveness, and have been acknowledged as the most dominant method for image classification. However, a severe drawback of d…
Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification
Ying Bi, Bing Xue, Mengjie Zhang
Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effectiv…
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Ying Bi, Bing Xue, Pablo Mesejo +2
Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse…
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
Christian Raymond, Qi Chen, Bing Xue +1
In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained und…
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Nan Li, Lianbo Ma, Guo Yu +3
Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often n…
Architecture Augmentation for Performance Predictor Based on Graph Isomorphism
Xiangning Xie, Yuqiao Liu, Yanan Sun +2
Neural Architecture Search (NAS) can automatically design architectures for deep neural networks (DNNs) and has become one of the hottest research topics in the current machine lea…
Deep Learning Model with GA based Feature Selection and Context Integration
Ranju Mandal, Basim Azam, Brijesh Verma +1
Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Many top-performing methods for image segmentation are bas…
Genetic Programming for Manifold Learning: Preserving Local Topology
Andrew Lensen, Bing Xue, Mengjie Zhang
Manifold learning methods are an invaluable tool in today's world of increasingly huge datasets. Manifold learning algorithms can discover a much lower-dimensional representation (…
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
Xiangning Xie, Yuqiao Liu, Yanan Sun +3
Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few ye…
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning
Ying Bi, Bing Xue, Mengjie Zhang
Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask problem be…
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising
Yuqiao Liu, Yanan Sun, Bing Xue +1
Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classifica…
A Survey on Evolutionary Neural Architecture Search
Yuqiao Liu, Yanan Sun, Bing Xue +3
Deep Neural Networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed…
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification
Bin Wang, Bing Xue, Mengjie Zhang
Deep convolutional neural networks have demonstrated promising performance on image classification tasks, but the manual design process becomes more and more complex due to the fas…
A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems
Isidro M. Alvarez, Trung B. Nguyen, Will N. Browne +1
Learning classifier systems (LCSs) originated from cognitive-science research but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extra…
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Trung B. Nguyen, Will N. Browne, Mengjie Zhang
Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-base…
Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions
Trung B. Nguyen, Will N. Browne, Mengjie Zhang
A major goal of machine learning is to create techniques that abstract away irrelevant information. The generalisation property of standard Learning Classifier System (LCS) removes…
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML
Benjamin Patrick Evans, Bing Xue, Mengjie Zhang
A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are s…
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation
Andrew Lensen, Bing Xue, Mengjie Zhang
Data visualisation is a key tool in data mining for understanding big datasets. Many visualisation methods have been proposed, including the well-regarded state-of-the-art method t…
Improving generalisation of AutoML systems with dynamic fitness evaluations
Benjamin Patrick Evans, Bing Xue, Mengjie Zhang
A common problem machine learning developers are faced with is overfitting, that is, fitting a pipeline too closely to the training data that the performance degrades for unseen da…
Multi-Objective Genetic Programming for Manifold Learning: Balancing Quality and Dimensionality
Andrew Lensen, Mengjie Zhang, Bing Xue
Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of…
Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems
Jordan MacLachlan, Yi Mei, Juergen Branke +1
Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisati…