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Auxiliary objectives may be used to reduce number of iterations of an evolutionary algorithm (EA). The corresponding approach is called multi-objectivization. We consider two multi-objectivization methods: EA+RL and MOEA+RL, where MOEA is a multi-objective EA, RL is reinforcement learning. In these methods, RL is used to select an objective during optimization process. In EA+RL only the selected objective is optimized, so a single-objective EA is used. In MOEA+RL the selected objective is optimized together with the target objective. Previously in these methods, RL for stationary environments was used. Recently, a new non-stationary RL algorithm was proposed. This algorithm was specially developed for the case when behaviour of auxiliary objectives changes during optimization process. However, this RL algorithm was tested only with EA+RL on some simple problems. In the present work we apply EA+RL and MOEA+RL with stationary and non-stationary RL to the travelling salesman problem (TSP) and compare them with the previously used multi-objectivization methods. We also analyze different types of auxiliary objectives for TSP. For the most of the considered problem instances, EA+RL and M

Author

Irina Petrova; Arina Buzdalova

Publisher

ACM

Published

2015

Language

EN