Recent Development in Automatic Parameter Tuning for Metaheuristics

Parameter tuning is an optimization problem with the objective of finding good static parameter settings before the execution of a metaheuristic on a problem at hand. The requirement of tuning multiple control parameters, combined with the stochastic nature of the algorithms, make parameter tuning a non-trivial problem. To make things worse, one parameter vector allowing the algorithm to solve all optimization problems to the best of its potential is verifiable non-existent, as can be inferred from the no free lunch theorem of optimization. Manual tuning can be conducted, with the drawback of being very time consuming and failure prone. Hence, means for automated parameter tuning are required. This paper serves as an overview about recent work within the field of automated parameter tuning.