The cross-entropy method is a versatile heuristic tool for solving difficult estimation and
optimization problems, based on Kullback–Leibler (or cross-entropy) minimization. As an …

" This textbook is a well-rounded, rigorous, and informative work presenting the mathematics
behind modern machine learning techniques. It hits all the right notes: the choice of topics is …

In recent years, the cross-entropy method has been successfully applied to a wide range of
discrete optimization tasks. In this paper we consider the cross-entropy method in the …

This paper is concerned with motion planning for non-linear robotic systems operating in
constrained environments. A method for computing high-quality trajectories is proposed …

The concept of using sensors on a passing vehicle, rather than on the bridge, is known as'
Drive‐by'damage detection. The newly developed traffic speed deflectometer (TSD) is a …

In recent years, heuristic algorithms have been successfully applied to solve clustering and
classification problems. In this paper, gravitational search algorithm (GSA) which is one of …

The EM algorithm is a very powerful optimization method and has become popular in many
fields. Unfortunately, EM is only a local optimization method and can get stuck in sub-optimal …

This paper describes a novel method to determine a road profile through the analysis of
accelerations in a passing vehicle. A direct integration algorithm is proposed to determine …

In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy Logic
Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities in the GPS …

This study introduces a novel method to determine apparent profile of the track and detect
railway bridge condition using sensors on in-service trains. The concept uses a type of …