# Cross-entropy method

## Papers overview

Semantic Scholar uses AI to extract papers important to this topic.

Highly Cited

2012

Highly Cited

2012

- I. J. Robotics Res.
- 2012

This paper is concerned with motion planning for non-linear robotic systems operating in constrained environments. A method forâ€¦Â (More)

Is this relevant?

2011

2011

- 2011

The cross-entropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based onâ€¦Â (More)

Is this relevant?

2007

2007

- IEEE Transactions on Reliability
- 2007

Consider a network of unreliable links, each of which comes with a certain price, and reliability. Given a fixed budget, whichâ€¦Â (More)

Is this relevant?

Highly Cited

2006

Highly Cited

2006

- Neural Computation
- 2006

The cross-entropy method is an efficient and general optimization algorithm. However, its applicability in reinforcement learningâ€¦Â (More)

Is this relevant?

Highly Cited

2005

Highly Cited

2005

- Annals OR
- 2005

An alternate formulation of the classical vehicle routing problem with stochastic demands (VRPSD) is considered. We propose a newâ€¦Â (More)

Is this relevant?

Review

2005

Review

2005

- Annals OR
- 2005

The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare eventâ€¦Â (More)

Is this relevant?

2005

2005

- Annals OR
- 2005

Consider a network of unreliable links, modelling for example a communication network. Estimating the reliability of the networkâ€¦Â (More)

Is this relevant?

Highly Cited

2004

Highly Cited

2004

- Information Science and Statistics
- 2004

The cross-entropy method is a recent versatile Monte Carlo technique. This article provides a brief introduction to the crossâ€¦Â (More)

Is this relevant?

Highly Cited

2004

Highly Cited

2004

- 2004

In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In thisâ€¦Â (More)

Is this relevant?

Highly Cited

2003

Highly Cited

2003

- ICML
- 2003

We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead ofâ€¦Â (More)

Is this relevant?