Investigation for using different penalty functions (L1 absolute value penalty or lasso, L2 standard weight decay or ridge regression, weight elimination etc.) on the weights for logistic regression for classification. 5 data sets from UCI Machine Learning Repository were used.

Exact penalty methods for the solution of constrained optimization problems are based on the construction of a function whose unconstrained minimizing points are also solution of the constrained problem. In the rst part of this paper we recall some deenitions concerning exactness properties of penalty functions, of barrier functions, of augmented Lagrangian functions, and discuss under which as...

In this paper we discuss the use of non-stationary penalty functions to solve general nonlinear programming problems (NP) using real-valued GAS. The non-stationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty increases it puts more and more selective pressure on the GA to find a feasible solution. The ideas...

The problem of minimizing by genetic algorithms the weight of a composite laminate subjected to various failure constraints is considered. Constraints are accounted for through penalty functions. The amount of penalty for each constraint violation is typically controlled by a penalty parameter that has a crucial innuence on the performance of the genetic algorithm. An optimal value of each pena...

We study a nonlinear exact penalization for optimization problems with a single constraint. The penalty function is constructed as a convolution of the objective function and the constraint by means of IPH (increasing positively homogeneous) functions. The main results are obtained for penalization by strictly IPH functions. We show that some restrictive assumptions, which have been made in ear...

In this paper, we consider Newton's method for solving the system of necessary optimality conditions of optimization problems with equality and inequality constraints. The principal drawbacks of the method are the need for a good starting point, the inability to distinguish between local maxima and local minima, and, when inequality constraints are present, the necessity to solve a quadratic pr...

In this paper we study penalized regression splines (P-splines), which are low–order basis function splines with a penalty to avoid undersmoothing. Such P–splines are typically not spatially adaptive, and hence can have trouble when functions are varying rapidly. While frequentist methods are available to address this issue, no Bayesian techniques have been developed. Our approach is to model t...

In this paper, we consider a risk model with two independent classes of insurance risks and random incomes. We assume that the two independent claim counting processes are, respectively, the Poisson and the Erlang(2) process. When the individual premium sizes are exponentially distributed, the explicit expressions for the Laplace transforms of the expected discounted penalty functions are deriv...