Geodesy

Evolutionary algorithm

Gene

Benchmark (surveying)

Demography

Neuroscience

Biology

Statistics

Human multitasking

Symbolic regression

Genetic programming

Evolutionary programming

Economics

Scalability

Biochemistry

Computer science

Evolutionary computation

Regression

Database

Task (project management)

Mathematics

Management

Geography

Population

Sociology

Artificial intelligence

Programming language

Machine learning

Chromosome

IEEE Transactions on Systems Man and Cybernetics Systems

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, multifactorial 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.

Multifactorial Genetic Programming for Symbolic Regression Problems