A hyper-heuristic with a round robin neighbourhood selection
Lecture Notes in Computer Science
An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-37198-1_1
Proceedings of the 13th European Conference, EvoCOP 2013, Vienna, Austria, 3-5 April 2013
Vol. 7832 (Evolutionary Computation in Combinatorial Optimization), pp. 1 - 12