- author = "Jinghui Zhong and Liang Feng and Wentong Cai and Yew-Soon Ong",
- journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems",
- title = "Multifactorial Genetic Programming for Symbolic Regression Problems",
- year = "2020",
- volume = "50",
- number = "11",
- pages = "4492--4505",
- month = nov,
- keywords = "genetic algorithms, genetic programming, GP, multifactorial evolutionary algorithm, MFEA, multifactorial optimization, MFO, multitask learning, MTL, symbolic regression problem, SRP",
- ISSN = "2168-2216",
-
DOI = "
doi:10.1109/TSMC.2018.2853719",
- size = "14 pages",
- abstract = "Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multi-factorial optimization (MFO) has been proposed as a new evolutionary paradigm toward evolutionary multitasking. It intends to conduct evolutionary search on multiple tasks in one independent run. To enable multitasking GP, in this paper, we propose a novel multifactorial GP (MFGP) algorithm. To the best of our knowledge, this is the first attempt in the literature to conduct multitasking GP using a single population. The proposed MFGP consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously, and new evolutionary mechanisms for MFO based on self-learning gene expression programming. Further, comprehensive experimental studies are conducted on multitask scenarios consisting of commonly used GP benchmark problems and real world applications. The obtained empirical results confirmed the efficacy of the proposed MFGP.",
-
notes = "Guangdong Provincial Key Laboratory of Computational
Intelligence and Cyberspace Information, School of
Computer Science and Engineering, South China
University of Technology, Guangzhou 510640, China.
Also known as \cite{8419217}",
