Paper Info
Title
Selection of Auxiliary Objectives in the Travelling Salesman Problem using Reinforcement Learning
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.
Year
DOI
Venue
2015
10.1145/2739482.2764646
GECCO (Companion)
Field
DocType
Citations 
Mathematical optimization,Evolutionary algorithm,Computer science,Travelling salesman problem,Artificial intelligence,Machine learning,Reinforcement learning
Conference
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Irina Petrova1162.53
Arina Buzdalova2619.42