In a large number of experimental problems the high dimensionality of the search space and economical constraints can severely limit the number of experiment points that can be tested. Under this constraints, optimization techniques perform poorly in particular when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from advanced statistics and optimization algorithms to effectively explore a combinatorial search space sampling a limited number of experimental points. To this purpose we propose the Naïve Bayes Ant Colony Optimization (NACO) procedure. We tested its performance in a simulation study.
Borrotti, M., Poli, I. (2013). Naïve Bayes Ant Colony Optimization for Experimental Design. In Synergies of Soft Computing and Statistics for Intelligent Data Analysis (pp.489-497). Springer Nature [10.1007%2F978-3-642-33042-1_52].
Naïve Bayes Ant Colony Optimization for Experimental Design
Borrotti, M;
2013
Abstract
In a large number of experimental problems the high dimensionality of the search space and economical constraints can severely limit the number of experiment points that can be tested. Under this constraints, optimization techniques perform poorly in particular when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from advanced statistics and optimization algorithms to effectively explore a combinatorial search space sampling a limited number of experimental points. To this purpose we propose the Naïve Bayes Ant Colony Optimization (NACO) procedure. We tested its performance in a simulation study.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
