The vast size of real world stochastic programming instances requires sampling to make them practically solvable. In this paper we extend the understanding of how sampling affects ...
We derive a new generalization of lowest common ancestors (LCAs) in dags, called the lowest single common ancestor (LSCA). We show how to preprocess a static dag in linear time su...
We propose a novel algorithm called GA-MDP for solving the frequency assigment problem. GA-MDP inherits the spirit of genetic algorithms with an adaptation of Markov Decision Proc...
The maximum-likelihood decoding problem is known to be NP-hard for general linear and Reed-Solomon codes [1, 4]. In this paper, we introduce the notion of A-covered codes, that is...
Abstract. In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm o...