Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem.

• Type: Journal
• Year: 2010
• Where: SEAL
• Authors: Karel Crombecq, Tom Dhaene
• Keywords: Genetic Algorithms, Machine Learning, Monte Carlo, Optimization-based Approach, SEAL 2010