A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective

• URL: http://arxiv.org/abs/2404.14228v1
• Date: Mon, 22 Apr 2024 14:38:58 GMT
• Title: A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective
• Authors: Mingyu Huang, Ke Li,
• Abstract summary: We build a knowledge graph that encapsulates more than 5,400 papers, 10,000 authors, 400 venues, and 1,600 institutions for MOEA/D research. We also explore the collaboration and citation networks of MOEA/D, uncovering hidden patterns in the growth of literature.
• Score: 4.322038460697958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the second part of the two-part survey series on decomposition-based evolutionary multi-objective optimization where we mainly focus on discussing the literature related to multi-objective evolutionary algorithms based on decomposition (MOEA/D). Complementary to the first part, here we employ a series of advanced data mining approaches to provide a comprehensive anatomy of the enormous landscape of MOEA/D research, which is far beyond the capacity of classic manual literature review protocol. In doing so, we construct a heterogeneous knowledge graph that encapsulates more than 5,400 papers, 10,000 authors, 400 venues, and 1,600 institutions for MOEA/D research. We start our analysis with basic descriptive statistics. Then we delve into prominent research/application topics pertaining to MOEA/D with state-of-the-art topic modeling techniques and interrogate their sptial-temporal and bilateral relationships. We also explored the collaboration and citation networks of MOEA/D, uncovering hidden patterns in the growth of literature as well as collaboration between researchers. Our data mining results here, combined with the expert review in Part I, together offer a holistic view of the MOEA/D research, and demonstrate the potential of an exciting new paradigm for conducting scientific surveys from a data science perspective.

Related papers

• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• MMSci: A Multimodal Multi-Discipline Dataset for PhD-Level Scientific Comprehension [59.41495657570397]
  We collected a multimodal, multidisciplinary dataset from open-access scientific articles published in Nature Communications journals. This dataset spans 72 scientific disciplines, ensuring both diversity and quality. We created benchmarks with various tasks and settings to comprehensively evaluate LMMs' capabilities in understanding scientific figures and content.
  arXiv  Detail & Related papers  (2024-07-06T00:40:53Z)
• A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models [43.37740735934396]
  Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs) This survey provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts.
  arXiv  Detail & Related papers  (2024-06-17T07:52:32Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs. We comprehensively survey over 250 scientific LLMs, discuss their commonalities and differences, as well as summarize pre-training datasets and evaluation tasks for each field and modality.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• Research Topic Flows in Co-Authorship Networks [0.0]
  We propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information) We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics.
  arXiv  Detail & Related papers  (2022-06-16T07:45:53Z)
• Enhancing Identification of Structure Function of Academic Articles Using Contextual Information [6.28532577139029]
  This paper takes articles of the ACL conference as the corpus to identify the structure function of academic articles. We employ the traditional machine learning models and deep learning models to construct the classifiers based on various feature input. Inspired by (2), this paper introduces contextual information into the deep learning models and achieved significant results.
  arXiv  Detail & Related papers  (2021-11-28T11:21:21Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• Evaluating the state-of-the-art in mapping research spaces: a Brazilian case study [0.0]
  Two recent works propose methods for creating research maps from scientists' publication records. We evaluate these models' ability to predict whether a given entity will enter a new field. We conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.
  arXiv  Detail & Related papers  (2021-04-07T18:14:41Z)
• What's New? Summarizing Contributions in Scientific Literature [85.95906677964815]
  We introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work. We extend the S2ORC corpus of academic articles by adding disentangled "contribution" and "context" reference labels. We propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs.
  arXiv  Detail & Related papers  (2020-11-06T02:23:01Z)
• Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention [21.93889297841459]
  We propose a novel entity recognition model, called MDER, which is able to effectively extract the method and dataset entities from scientific papers. We evaluate the proposed model on datasets constructed from the published papers of four research areas in computer science, i.e., NLP, CV, Data Mining and AI.
  arXiv  Detail & Related papers  (2020-10-26T13:38:43Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.