Abstract
The greedy method is a well-known technique for solving various problems so as to optimize (minimize or maximize) specific objective functions. As pointed by Dechter et al [1], greedy method is a controlled search strategy that selects the next state to achieve the largest possible improvement in the value of some measure which may or may not be the objective function. In recent years, many modern algorithms or heuristics have been introduced in the literature, and many types of improved greedy algorithms have been proposed. In fact, the core of many Meta-heuristic such as simulated annealing and genetic algorithms are based on greedy strategy. “The one with maximum benefit from multiple choices is selected” is the basic idea of greedy method. A greedy method arrives at a solution by making a sequence of choices, each of which simply looks the best at the moment. We refer to the resulting algorithm by this principle the basic greedy (BG) algorithm, the details of which can be described as follow:
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