@inproceedings{51d06abafcb2448da0bf6a9968de9982,
title = "Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems",
abstract = "Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants.",
keywords = "evolutionary algorithm, knowledge transfer, multi-task optimization",
author = "Wang, {Xiao Ling} and Qi Kang and Zhou, {Meng Chu}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022 ; Conference date: 15-12-2022 Through 18-12-2022",
year = "2022",
doi = "10.1109/ICNSC55942.2022.10004137",
language = "English (US)",
series = "ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control: Autonomous Intelligent Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control",
address = "United States",
}