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

On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems