TY - JOUR
T1 - Evolutionary computation and big data
T2 - Key challenges and future directions
AU - Cheng, Shi
AU - Liu, Bin
AU - Shi, Yuhui
AU - Jin, Yaochu
AU - Li, Bin
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Over the past few years, big data analytics has received increasing attention in all most all scientific research fields. This paper discusses the synergies between big data and evolutionary computation (EC) algorithms, including swarm intelligence and evolutionary algorithms. We will discuss the combination of big data analytics and EC algorithms, such as the application of EC algorithms to solving big data analysis problems and the use of data analysis methods for designing new EC algorithms or improving the performance of EC algorithms. Based on the combination of EC 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 EC algorithms could be analyzed via the data analytics along the optimization process, a crucial entity in EC algorithms. Key challenges and future directions in combining big data and EC algorithms are discussed.
AB - Over the past few years, big data analytics has received increasing attention in all most all scientific research fields. This paper discusses the synergies between big data and evolutionary computation (EC) algorithms, including swarm intelligence and evolutionary algorithms. We will discuss the combination of big data analytics and EC algorithms, such as the application of EC algorithms to solving big data analysis problems and the use of data analysis methods for designing new EC algorithms or improving the performance of EC algorithms. Based on the combination of EC 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 EC algorithms could be analyzed via the data analytics along the optimization process, a crucial entity in EC algorithms. Key challenges and future directions in combining big data and EC algorithms are discussed.
KW - Big data analytics
KW - Data science
KW - Evolutionary algorithms
KW - Evolutionary computation
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85008502162&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-40973-3_1
DO - 10.1007/978-3-319-40973-3_1
M3 - Article
AN - SCOPUS:85008502162
SN - 0302-9743
VL - 9714 LNCS
SP - 3
EP - 14
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -