Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference

A Hybrid Neuroevolutionary Approach to the Design of Convolutional Neural Networks for 2D and 3D Medical Image Segmentation

This thesis highlights the development and evaluation of a hybrid neuroevolutionary approach for designing Convolutional Neural Networks (CNNs) for image classification and segmentation tasks. We integrate Cartesian Genetic Programming (CGP) with Novelty Search and Simulated Annealing algorithms to optimize the CNN architectures efficiently. The challenge lies in reducing the computational demands and inefficiencies of traditional Neural Architecture Search (NAS) techniques. To address this, a flexible framework based on CGP is utilized for evolving network architectures. Novelty Search facilitates the exploration of varied architectural landscapes, promoting diversity of solutions. Simulated Annealing further refines these solutions, optimizing the balance between exploring new possibilities and exploiting known good solutions within the search space. Our experiments, conducted on benchmark datasets such as DRIVE and MSD, demonstrate the method’s effectiveness in generating competitive segmentation models. On the DRIVE dataset, our models achieved Dice Similarity Coefficients (DSC) of 0.828 and 0.814, and Intersection over Union (IoU) scores of 0.716 and 0.736, respectively. For the MSD dataset, our models exhibited DSC scores up to 0.924 for the Heart task, showcasing the potential of our method in handling complex segmentation challenges across different medical imaging modalities. The significance of this research lies in its hybrid approach that efficiently navigates the search space for CNN architectures, thus reducing number of fitness evaluations while achieving near state of art performance. Future work will explore enhancing the algorithm’s effectiveness through advanced data preprocessing techniques, and the exploration of more complex network layers. Our findings highlight the potential of evolutionary algorithms and local search in advancing automated CNN design for medical image segmentation, offering a promising direction for future research in the field

Survey on highly imbalanced multi-class data

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multi-class data

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students