Above are papers related to the following paper: [Paper_ID] Paper #1131754. 	[Title] An Evolutionary Approach to Time Constrained Routing Problems [Abstract] Routing problems are an important class of planning problems. Usually there are many different constraints and optimization criteria involved, and it is difficult to find general methods for solving routing problems. We propose an evolutionary solver for such planning problems. An instance of this solver has been tested on a specific routing problem with time constraints. The performance of this evolutionary solver is compared to a biased random solver and a biased hillclimber solver. Results show that the evolutionary solver performs significantly better than the other two solvers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #654519. 	[Title] Evolutionary Computation in Air Traffic Control Planning [Abstract] Air Traffic Control is involved in the real-time planning of aircraft trajectories. This is a heavily constrained optimization problem. We concentrate on free-route planning, in which aircraft are not required to fly over way points. The choice of a proper representation for this real-world problem is non-trivial. We propose a two level representation: one level on which the evolutionary operators work, and a derived level on which we do calculations. Furthermore we show that a specific choice of the fitness function is important for finding good solutions to large problem instances. We use a hybrid approach in the sense that we use knowledge about air traffic control by using a number of heuristics. We have built a prototype of a planning tool, and this resulted in a flexible tool for generating a free-route planning of low cost, for a number of aircraft. Please summarise all the given information.

Paper #1131754 has the title `An Evolutionary Approach to Time Constrained Routing Problems  
` and the abstract `Routing problems are an important class of planning problems. Usually there are many different constraints and optimization criteria involved, and it is difficult to find general methods for solving routing problems. We propose an evolutionary solver for such planning problems. An instance of this solver has been tested on a specific routing problem with time constraints. The performance of this evolutionary solver is compared to a biased random solver and a biased hillclimber solver. Results show that the evolutionary solver performs significantly better than the other two solvers. 
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Question: Which category should Paper #1131754 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1131754 has the title `An Evolutionary Approach to Time Constrained Routing Problems  
` and the abstract `Routing problems are an important class of planning problems. Usually there are many different constraints and optimization criteria involved, and it is difficult to find general methods for solving routing problems. We propose an evolutionary solver for such planning problems. An instance of this solver has been tested on a specific routing problem with time constraints. The performance of this evolutionary solver is compared to a biased random solver and a biased hillclimber solver. Results show that the evolutionary solver performs significantly better than the other two solvers. 
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Question: Which category should Paper #1131754 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms