title: Choosing the appropriate forecasting model for predictive parameter control.
abstract: All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.
authors.affiliation: Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia aldeida.aleti@monash.edu.
authors.forename: Aldeida
authors.initials: A
authors.lastname: Aleti
authors.forename: Irene
authors.initials: I
authors.lastname: Moser
authors.forename: Indika
authors.initials: I
authors.lastname: Meedeniya
authors.forename: Lars
authors.initials: L
authors.lastname: Grunske
mesh_terms: D000465:Algorithms; D003205:Computing Methodologies; D005544:Forecasting / Q000379:methods*; D016014:Linear Models; D008962:Models, Theoretical; D013269:Stochastic Processes; D013997:Time Factors