Response surface methodology to tune artificial neural network hyper‐parameters

TL;DR: In this article , the performance of a new metaheuristic, MPA (Marine Predators Algorithm) integrated with RSM has also been tested to obtain maximum decolorization.

TL;DR: In this article , the effect of process parameters and predictive accuracy of the ANN and response surface methodology (RSM) was analyzed for friction stir welding (FSW) joint properties.

TL;DR: In this paper , a comparative analysis is provided on support vector machines (SVM)-based methods, k-nearest neighbors (KNN)-based method, and ensemble-based method to analyze system marginal price (SMP).

TL;DR: Using a practical approach, this book discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques.

TL;DR: This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.

TL;DR: This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms.

TL;DR: In this article, the authors present a Second-Order Response Surface Methodology (SRSM) for response surface design, which is based on Maxima and Ridge systems with second-order response surfaces.