This paper gives a theoretical and empirical analysis of the time complexity of genetic algorithms (GAs) on problems with exponentially scaled building blocks. It is important to ...
Fernando G. Lobo, David E. Goldberg, Martin Pelika...
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
We propose a branch and prune algorithm that is able to compute inner and outer approximations of the solution set of an existentially quantified constraint where existential param...
We give the first optimal algorithm for estimating the number of distinct elements in a data stream, closing a long line of theoretical research on this problem begun by Flajolet...
We give efficient algorithms to sample uniformly, and count approximately, the solutions to a zero-one knapsack problem. The algorithm is based on using dynamic programming to pro...