Hao-Feng Wu

Experiments across synthetic benchmarks and a real-world robot arm control problem, together with evaluations under different acquisition functions and evolutionary multitasking, demonstrate the effectiveness and generality of ICG-MTO for few-shot multitask optimization.

This paper model MTO as a sequence of sequential transfer optimization problems, concentrating evaluations on a single target per iteration, and proposes a likelihood-informed task prioritization mechanism to maximize transfer utility by identifying the task most likely ready for knowledge integration.