  The performance of different mutation operators is usually evaluated in
conjunc-tion with specific parameter settings of genetic algorithms and target
problems. Most studies focus on the classical genetic algorithm with different
parameters or on solving unconstrained combinatorial optimization problems such
as the traveling salesman problems. In this paper, a subpopulation-based
genetic al-gorithm that uses only mutation and selection is developed to solve
multi-robot task allocation problems. The target problems are constrained
combinatorial optimization problems, and are more complex if cooperative tasks
are involved as these introduce additional spatial and temporal constraints.
The proposed genetic algorithm can obtain better solutions than classical
genetic algorithms with tournament selection and partially mapped crossover.
The performance of different mutation operators in solving problems
without/with cooperative tasks is evaluated. The results imply that inversion
mutation performs better than others when solving problems without cooperative
tasks, and the swap-inversion combination performs better than others when
solving problems with cooperative tasks.
