The problem of expressing and supporting classical greedy algorithms in Datalog has been the focus of many signi cant research e orts that have produced very interesting solutions for particular algorithms. But we still lack a general treatment that characterizes the relationship of greedy algorithms to non-monotonic theories and leads to asymptotically optimal implementations. In this paper, w...

introduction several methods have been developed to aid in selecting a network of biodiversity protected areas. one of these methods is artificial intelligence which includes a number of different computer algorithms that use an objective function to find the best solution. an algorithm is a mathematical process or a set of rules used for problem solving. recently, site selection algorithms hav...

This paper present two meta heuristics, reverse greedy and future aware greedy, which are variants of the greedy algorithm. Both are based on the observation that guessing the impact of future selections is useful for the current selection. While the greedy algorithm makes the best local selection given the past, future aware greedy makes the best local selection given the past and the estimate...

Abstract. We consider a generalization of the well known greedy algorithm, called mstep greedy algorithm, where m elements are examined in each iteration. When m = 1 or 2, the algorithm reduces to the standard greedy algorithm. For m = 3 we provide a complete characterization of the independence system, called trioid, where the m-step greedy algorithm guarantees an optimal solution for all weig...

Iterated local search is a stochastic local search (SLS) method that combines a perturbation step with an embedded local search algorithm. In this article, we propose a new way of hybridizing iterated local search. It consists in using an iterated local search as the embedded local search algorithm inside another iterated local search. This nesting of local searches and iterated local searches ...