When evolutionary algorithms are used for solving numerical constrained optimization problems, how to deal with the relationship between feasible and infeasible individuals can dir...
This paper presents a problem-independent framework that uni es various mechanisms for solving discrete constrained nonlinear programming (NLP) problems whose functions are not ne...
— Motivated by some crowd motion models in the presence of noise, we consider an optimal control problem governed by the Fokker-Planck equation. We sketch optimality conditions b...
Abstract. Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence,...
We present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in a principled wa...