We show that our approach corresponds to a specific scheme of ridge regression with a total matrix added to the matrix of correlations. One of the main goals of multiple regression modeling is to assess the imp
Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. ...
In this chapter we examine how multi-objective genetic programming can be used to perform symbolic regression and compare its performance to single-objective genetic programming. Multi-objective optimization is implemented by using a slightly adapted version of NSGA-II, where the optimization objectives ...
Other authors also consider respiratory problems, such as the model presented in [13] using a respiratory score, the CapeTown score, which extends the Baux score with an inhalation score [14], or the score based on logistic regression presented in [15]. Some authors suggest that, for ...
Incremental regression testing. In: Proceedings of the Conference on Software Maintenance. ICSM ’93. Washington: IEEE Computer Society: 1993. p. 348–357. Google Scholar Rothermel G, Harrold MJ. A safe, efficient regression test selection technique. ACM Trans Softw Eng Methodol. 1997; 6(2)...
another GP51with a Matérn 5/2 kernel to account for the high variability in the parameter space (Fig.1c)38,52. For approach 2 (GPSG-BO), we selected a two-layer neural network53,54,55, multivariate adaptive regression splines56, and a random forest algorithm57,58as level 0 learners....
macosiosmachine-learningobjective-cneural-networkregressionrankingsupervised-learningalgorithm-implementationsmulti-objective UpdatedJan 22, 2017 Objective-C mit-gfx/PGMORL Star103 Code Issues Pull requests [ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control ...
& Xu, L. A multi-fidelity surrogate model based on extreme support vector regression: Fusing different fidelity data for engineering design. Eng. Comput. 40(2), 473–493. https://doi.org/10.1108/EC-10-2021-0583 (2023). Article Google Scholar Zhou, S. et al. On the convergence of ...
Regression testing is one of the most critical activities in the software maintenance process and its importance is twofold for evolutionary applications, e.g., modern flexible web-based applications. By increasing the complexity of application due to the rapid change, automatic evolutionary testing ...
And the R2 values of BPNN and SVR were significantly higher than those of the other three regression models, which meant that the errors of BPNN and SVR were smaller and the accuracy of BPNN and SVR were higher than those of the others. The R2 values of SVR were 0.5629, 0.7926, and ...