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» Sampling Bounds for Stochastic Optimization
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GECCO
2007
Springer
140views Optimization» more  GECCO 2007»
15 years 12 months ago
On the moments of the sampling distribution of particle swarm optimisers
A method is presented that allows one to exactly determine all the characteristics of a PSO’s sampling distribution and explain how it changes over time, in the presence stochas...
Riccardo Poli
ICCAD
2007
IEEE
96views Hardware» more  ICCAD 2007»
16 years 2 months ago
Monte-Carlo driven stochastic optimization framework for handling fabrication variability
Increasing effects of fabrication variability have inspired a growing interest in statistical techniques for design optimization. In this work, we propose a Monte-Carlo driven sto...
Vishal Khandelwal, Ankur Srivastava
RSS
2007
136views Robotics» more  RSS 2007»
15 years 7 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...
AIPS
2010
15 years 8 months ago
When Policies Can Be Trusted: Analyzing a Criteria to Identify Optimal Policies in MDPs with Unknown Model Parameters
Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space ro...
Emma Brunskill
ICPR
2010
IEEE
15 years 3 months ago
Efficient Polygonal Approximation of Digital Curves via Monte Carlo Optimization
A novel stochastic searching scheme based on the Monte Carlo optimization is presented for polygonal approximation (PA) problem. We propose to combine the split-and-merge based lo...
Xiuzhuang Zhou, Yao Lu