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Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems | IEEE Conference Publication | IEEE Xplore

Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems

Publisher: IEEE

Abstract:

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms...View more

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.
Date of Conference: 15-18 December 2022
Date Added to IEEE Xplore: 12 January 2023
ISBN Information:
Publisher: IEEE
Conference Location: Shanghai, China

Funding Agency:


References

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