L
Liang Feng
Researcher at Chongqing University
Publications - 146
Citations - 5262
Liang Feng is an academic researcher from Chongqing University. The author has contributed to research in topics: Evolutionary algorithm & Optimization problem. The author has an hindex of 26, co-authored 126 publications receiving 3172 citations. Previous affiliations of Liang Feng include Nanjing University of Posts and Telecommunications & Nanyang Technological University.
Papers
More filters
Book
Extreme Learning Machine
Erik Cambria,Guang-Bin Huang,Liyanaarachchi Lekamalage Chamara Kasun,Hongming Zhou,Chi-Man Vong,Jiarun Lin,Jianping Yin,Zhiping Cai,Qiang Liu,Kuan Li,Victor C. M. Leung,Liang Feng,Yew-Soon Ong,Meng-Hiot Lim,Anton Akusok,Amaury Lendasse,Francesco Corona,Rui Nian,Yoan Miche,Paolo Gastaldo,Rodolfo Zunino,Sergio Decherchi,Xuefeng Yang,Kezhi Mao,Beom-Seok Oh,Jehyoung Jeon,Kar-Ann Toh,Andrew Beng Jin Teoh,Jaihie Kim,Hanchao Yu,Yiqiang Chen,Junfa Liu +31 more
TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.
Journal ArticleDOI
Multifactorial Evolution: Toward Evolutionary Multitasking
TL;DR: This paper formalizes the concept of evolutionary multitasking and proposes an algorithm to handle multiple optimization problems simultaneously using a single population of evolving individuals and develops a cross-domain optimization platform that allows one to solve diverse problems concurrently.
Journal ArticleDOI
Multiobjective Multifactorial Optimization in Evolutionary Multitasking
TL;DR: This paper presents a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization, which leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics.
Journal ArticleDOI
Consistencies and contradictions of performance metrics in multiobjective optimization.
TL;DR: Experimental results indicated that these six major MOO metrics show high consistencies when Pareto fronts (PFs) are convex, whereas they show certain contradictions on concave PFs.
Journal ArticleDOI
Insights on Transfer Optimization: Because Experience is the Best Teacher
TL;DR: A general formalization of transfer optimization is introduced, based on which the conceptual realizations of the paradigm are classified into three distinct categories, namely sequential transfer , multitasking, and multiform optimization.