Naïve Bayes Ant Colony Optimization for Experimental Design
作者： Matteo Borrotti , Irene Poli
DOI: 10.1007/978-3-642-33042-1_52
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摘要: In a large number of experimental problems the high dimensionality search space and economical constraints can severely limit experiment points that be tested. Under this constraints, optimization techniques perform poorly in particular when little priori knowledge is available. work we investigate possibility combining approaches from advanced statistics algorithms to effectively explore combinatorial sampling limited points. To purpose propose Naive Bayes Ant Colony Optimization (NACO) procedure. We tested its performance simulation study.
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