Version 2.5.8 of the Sequential Parameter Optimization Toolbox is available. SPOT is set of tools for model-based optimization and tuning of algorithms (hyperparameter tuning). It includes surrogate models, optimizers, and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources.
- fixes a bug that prevents passing additional arguments to the objective function and
- implements the new function ShiftedSphere: f = sum (x-a)^2 (can be used for testing the bug from above, i.e., parameters are passed correctly to the objective function)
- Furthermore, SPOTVignette Nutshell contains example code for the GECCO Industrial Challenge