NDLI: Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning

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Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning

Content Provider	ACM Digital Library
Author	Buzdalova, Arina Petrova, Irina
Abstract	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 MOEA+RL for non-stationary environment perform better than the other considered methods.
Starting Page	1455
Ending Page	1456
Page Count	2
File Format	PDF
ISBN	9781450334884
DOI	10.1145/2739482.2764646
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-07-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multi-objectivization Helper-objectives Non-stationarity
Content Type	Text
Resource Type	Article

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