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On merging sequencing and scheduling theory with genetic algorithms to solve stochastic job shops.
(1997)
The stochastic job shop problem was solved using two genetic algorithms. The first was a stochastic constrained genetic algorithm to minimize total tardiness and to evaluate chromosomes using probability Gantt charting. ...
Primal-dual techniques for nonlinear programming and applications to artificial neural network training.
(1997)
In this work, new developments in primal-dual techniques for general constrained non-linear programming problems are proposed. We first implement a modified version of the general nonlinear primal-dual algorithm that was ...
Data balancing approaches in quality, defect, and pattern analysis
(2023-05-12)
The imbalanced ratio of data is one of the most significant challenges in various industrial domains. Consequently, numerous data-balancing approaches have been proposed over the years. However, most of these data-balancing ...
Decomposition techniques for support vector machines training and applications.
(2002)
The theory of the Support Vector Machine (SVM) algorithm is based on statistical learning theory and can be applied to pattern recognition and regression. Training of SVMs leads to either a quadratic programming (QP) ...
Incremental kernel learning algorithms and applications.
(2006)
Since the Support Vector Machines (SVMs) were introduced in 1995, SVMs have been recognized as essential tools for pattern classification and function approximation. Numerous publications show that SVMs outperform other ...