Domain Adaptation Multitask Optimization

Xiaoling Wang, Qi Kang, Meng Chu Zhou, Siya Yao, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Multitask optimization (MTO) is a new optimization paradigm that leverages useful information contained in multiple tasks to help solve each other. It attracts increasing attention in recent years and gains significant performance improvements. However, the solutions of distinct tasks usually obey different distributions. To avoid that individuals after intertask learning are not suitable for the original task due to the distribution differences and even impede overall solution efficiency, we propose a novel multitask evolutionary framework that enables knowledge aggregation and online learning among distinct tasks to solve MTO problems. Our proposal designs a domain adaptation-based mapping strategy to reduce the difference across solution domains and find more genetic traits to improve the effectiveness of information interactions. To further improve the algorithm performance, we propose a smart way to divide initial population into different subpopulations and choose suitable individuals to learn. By ranking individuals in target subpopulation, worse-performing individuals can learn from other tasks. The significant advantage of our proposed paradigm over the state of the art is verified via a series of MTO benchmark studies.

Original languageEnglish (US)
Pages (from-to)4567-4578
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume53
Issue number7
DOIs
StatePublished - Jul 1 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Domain adaptation
  • evolutionary algorithm (EA)
  • knowledge transfer
  • machine learning
  • multitask optimization (MTO)

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