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In this paper, we propose a genetic algorithm-based method for evaluation of operational plans within effects-based planning. We formulate the effects-based planning problem as a bi-objective optimization problem, in which the distance from the initial state to the current state (g) and the distance from the current state to the desired end state (h) are minimized. To solve the problem, we adopt Non-dominated...
In this paper, we propose a genetic algorithm-based method for evaluation of operational plans within effects-based planning. We formulate the effects-based planning problem as a bi-objective optimization problem, in which the distance from the initial state to the current state (g) and the distance from the current state to the desired end state (h) are minimized. To solve the problem, we adopt Non-dominated...
The classic task assignment problem (AP) assigns m agents to n tasks, where each task is assigned to exactly one agent. This problem and many of its variations, including the case where a task is assigned to a group of agents working independently, have been discussed extensively in the literature. We consider a specific class of task assignment problems where each task is assigned to a group of collaborating...
Mutation testing is a technique in which all possible faulty versions of a program are generated for mutation analysis and to assess the test suit adequacy. In literature, most research on mutation testing has been focused on traditional programming paradigm. Some of the traditional mutation-operators are incorporated in mutation testing of object oriented systems. Recently class level mutation operators...
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