Survey on data science with population-based algorithms. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Cheng, Shi; Liu, Bin; Ting, T.; Qin, Quande; Shi, Yuhui; Huang, Kaizhu
• Is Part Of: Big data analytics. Volume 1:Issue 1(2016)
• Page Start: 1
• Page End: 20
• Publication Date: 2016-12
• Subjects: Big data analytics -- Data analysis -- Data science -- Evolutionary algorithms -- Swarm intelligence -- Population-based algorithm; Big data -- Periodicals; Biology -- Data processing -- Periodicals; 570.28557
• DOI: 10.1186/s41044-016-0003-3
• Languages: English
• ISSNs: 2058-6345

Abstract

Abstract This paper discusses the relationship between data science and population-based algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two categories of literature, which include population-based algorithms solving data analysis problem and utilizing data analysis methods in population-based algorithms. With the exponential increment of data, the data science, or more specifically, the big data analytics has gained increasing attention among researchers. New and more efficient algorithms should be designed to handle this massive data problem. Based on the combination of population-based algorithms and data mining techniques, we understand better the insights of data analytics, and design more efficient algorithms to solve real-world big data analytics problems. Also, the weakness and strength of population-based algorithms could be analyzed via the data analytics along the optimization process, a crucial entity in population-based algorithms.