Above are papers related to the following paper: [Paper_ID] Paper #1131549. 	[Title] An Artificial Life Model for Investigating the Evolution of Modularity [Abstract] To investigate the issue of how modularity emerges in nature, we present an Artificial Life model that allow us to reproduce on the computer both the organisms (i.e., robots that have a genotype, a nervous system, and sensory and motor organs) and the environment in which organisms live, behave and reproduce. In our simulations neural networks are evolutionarily trained to control a mobile robot designed to keep an arena clear by picking up trash objects and releasing them outside the arena. During the evolutionary process modular neural networks, which control the robot's behavior, emerge as a result of genetic duplications. Preliminary simulation results show that duplication-based modular architecture outperforms the nonmod-ular architecture, which represents the starting architecture in our simulations. Moreover, an interaction between mutation and duplication rate emerges from our results. Our future goal is to use this model in order to explore the relationship between the evolutionary emergence of modularity and the phenomenon of gene duplication. Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #41714. 	[Title] Issues in Evolutionary Robotics [Abstract] A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #190706. 	[Title] Selection for Wandering Behavior in a Small Robot [Abstract] We have evolved artificial neural networks to control the wandering behavior of small robots. The task was to touch as many squares in a grid as possible during a fixed period of time. A number of the simulated robots were embodied in small Lego (Trademark) robot, controlled by a Motorola (Trademark) 6811 processor; and their performance was compared to the simulations. We observed that: (a) evolution was an effective means to program control; (b) progress was characterized by sharply stepped periods of improvement, separated by periods of stasis that corresponded to levels of behavioral/computational complexity; and (c) the simulated and realized robots behaved quite similarly, the realized robots in some cases outperforming the simulated ones. Introducing random noise to the simulations improved the fit somewhat (from 0.73 to 0.79). Hybrid simulated/embodied selection regimes for evolutionary robots are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144212. 	[Title] Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects [Abstract] Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how control systems that perform a nontrivial sequence of behaviors can be obtained with this methodology by carefully designing the conditions in which the evolutionary process operates. In the experiment described in the paper, a mobile robot is trained to locate, recognize, and grasp a target object. The controller of the robot has been evolved in simulation and then downloaded and tested on the real robot. Please summarise all the given information.

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Paper #1131549 has the title `An Artificial Life Model for Investigating the Evolution of Modularity  
` and the abstract `To investigate the issue of how modularity emerges in nature, we present an Artificial Life model that allow us to reproduce on the computer both the organisms (i.e., robots that have a genotype, a nervous system, and sensory and motor organs) and the environment in which organisms live, behave and reproduce. In our simulations neural networks are evolutionarily trained to control a mobile robot designed to keep an arena clear by picking up trash objects and releasing them outside the arena. During the evolutionary process modular neural networks, which control the robot's behavior, emerge as a result of genetic duplications. Preliminary simulation results show that duplication-based modular architecture outperforms the nonmod-ular architecture, which represents the starting architecture in our simulations. Moreover, an interaction between mutation and duplication rate emerges from our results. Our future goal is to use this model in order to explore the relationship between the evolutionary emergence of modularity and the phenomenon of gene duplication.
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Question: Which category should Paper #1131549 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1131549 has the title `An Artificial Life Model for Investigating the Evolution of Modularity  
` and the abstract `To investigate the issue of how modularity emerges in nature, we present an Artificial Life model that allow us to reproduce on the computer both the organisms (i.e., robots that have a genotype, a nervous system, and sensory and motor organs) and the environment in which organisms live, behave and reproduce. In our simulations neural networks are evolutionarily trained to control a mobile robot designed to keep an arena clear by picking up trash objects and releasing them outside the arena. During the evolutionary process modular neural networks, which control the robot's behavior, emerge as a result of genetic duplications. Preliminary simulation results show that duplication-based modular architecture outperforms the nonmod-ular architecture, which represents the starting architecture in our simulations. Moreover, an interaction between mutation and duplication rate emerges from our results. Our future goal is to use this model in order to explore the relationship between the evolutionary emergence of modularity and the phenomenon of gene duplication.
`.
Question: Which category should Paper #1131549 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