  Multi-task learning uses auxiliary data or knowledge from relevant tasks to
facilitate the learning in a new task. Multi-task optimization applies
multi-task learning to optimization to study how to effectively and efficiently
tackle multiple optimization problems simultaneously. Evolutionary
multi-tasking, or multi-factorial optimization, is an emerging subfield of
multi-task optimization, which integrates evolutionary computation and
multi-task learning. This paper proposes a novel and easy-to-implement
multi-tasking genetic algorithm (MTGA), which copes well with significantly
different optimization tasks by estimating and using the bias among them.
Comparative studies with eight state-of-the-art single- and multi-task
approaches in the literature on nine benchmarks demonstrated that on average
the MTGA outperformed all of them, and had lower computational cost than six of
them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system
design is also proposed. Experiments on simultaneous optimization of type-1 and
interval type-2 fuzzy logic controllers for couple-tank water level control
demonstrated that the MTGA can find better fuzzy logic controllers than other
approaches.
