Adaptive Range Parameter Control


All existing stochastic optimisers such as Evolutionary Algorithms require parameterisation which has a significant influence on the algorithm's performance. In most cases, practitioners assign static values to variables after an initial tuning phase. This parameter tuning method requires experience the practitioner may not have and, when done conscientiously, is rather time-consuming. Also, the use of parameter values that remain constant over the optimisation process has been observed to achieve suboptimal results. This work presents a parameter control method which redefines variables repeatedly based on a separate optimisation process which receives its feedback from the primary optimisation algorithm. The feedback is used for a projection of the value performing well in the future. The parameter values are sampled from intervals which are adapted dynamically, a method which has proved particularly effective and outperforms all existing adaptive parameter controls significantly.