The problem of control synthesis is considered as machine learning control. paper proposes a mathematical formulation control, discusses approaches supervised and unsupervised by symbolic regression methods. principle small variation the basic solution presented to set up neighbourhood search increase efficiency Different methods such genetic programming, network operator, Cartesian binary prog...

It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present high performance overall and for class, even when minority class significantly lower. This paper presents genetic programming (GP) model with crossover-based oversampling technique dataset binary text classification. The aim of stu...

In his Ph.D. disseration, Feldman and his collaborators define the linear programming decoder for binary linear codes, which is a linear programming relaxation of the maximum-likelihood decoding problem. This decoder does not, in general, attain maximum-likelihood performance; however, the source of this discrepancy is known to be the presence of non-integral extreme points (vertices) within th...

This presentation has two purposes: (1) show operations researchers how they can apply quadratic binary programming to current problems in molecular biology, and (2) show formulations of some combinatorial optimization problems as integer programs. The former purpose is primary, and I wish to persuade researchers to enter this exciting frontier. The latter purpose is part of a work in progress....

The use of evolutionary programming for adaptive equalization of binary data bursts in a baseband digital communication system is studied. Evolutionary programming has been found to perform well for blind channel identification. In this paper it is shown that the usage of evolutionary programming techniques are beneficially also for adaptive channel equalization. We have also studied the effect...