Empirical divergence maximization is an estimation method similar to empirical risk minimization whereby the Kullback-Leibler divergence is maximized over a class of functions tha...
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
An important strength of learning classifier systems (LCSs) lies in the combination of genetic optimization techniques with gradient-based approximation techniques. The chosen app...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
Background: The inference of homology from statistically significant sequence similarity is a central issue in sequence alignments. So far the statistical distribution function un...
Abstract. We propose an analytic approach to approximate the survival probabilities of schemata under multi-point crossover and obtain its closed form. It gives a convenient way to...