In constraint programming one models a problem by stating constraints on acceptable solutions. The constraint model is then usually solved by interleaving backtracking search and ...
We study the average-case performance of algorithms for the binary knapsack problem. Our focus lies on the analysis of so-called core algorithms, the predominant algorithmic conce...
Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a ...
This paper presents a genetic algorithm (GA) with specialized encoding, initialization and local search genetic operators to optimize communication network topologies. This NPhard...
Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(x) given a budget. In our setting, it is not ...
Javad Azimi, Xiaoli Fern, Alan Fern, Elizabeth Bur...