Above are papers related to the following paper: [Paper_ID] Paper #1131565. 	[Title] GENE REGULATION AND BIOLOGICAL DEVELOPMENT IN NEURAL NETWORKS: AN EXPLORATORY MODEL [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593155. 	[Title] Cellular Encoding for Interactive Evolutionary Robotics [Abstract] Research in robotics programming is divided in two camps. The direct hand programmming approach uses an explicit model or a behavioral model ( subsumption architecture). The machine learning community uses neural network and/or genetic algorithm. We claim that hand programming and learning are complementary. The two approaches used together can be orders of magnitude more powerful than each approach taken separately. We propose a method to combine them both. It includes three concepts : syntactic constraints to restrict the search space, hand-made problem decomposition, hand given fitness. We use this method to solve a complex problem ( eight-legged locomotion). It needs 5000 less evaluations compared to when genetic algorithm are used alone. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #251756. 	[Title] Cellular Encoding Applied to Neurocontrol [Abstract] Neural networks are trained for balancing 1 and 2 poles attached to a cart on a fixed track. For one variant of the single pole system, only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of the problems are solved using a fixed architecture and using a new version of cellular encoding that evolves an application specific architecture with real-valued weights. The learning times and generalization capabilities are compared for neural networks developed using both methods. After a post processing simplification, topologies produced by cellular encoding were very simple and could be analyzed. Architectures with no hidden units were produced for the single pole and the two pole problem when velocity information is supplied as an input. Moreover, these linear solutions display good generalization. For all the control problems, cellular encoding can automatically generate architectures whose complexity and structure reflect the features of the problem to solve. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #195792. 	[Title] A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks [Abstract] This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimized by evolutionary algorithms. Direct Encoding encodes the weights for an a priori fixed neural network architecture. Cellular Encoding encodes both weights and the architecture of the neural network. In previous studies, Direct Encoding and Cellular Encoding have been used to create neural networks for balancing 1 and 2 poles attached to a cart on a fixed track. The poles are balanced by a controller that pushes the cart to the left or the right. In some cases velocity information about the pole and cart is provided as an input; in other cases the network must learn to balance a single pole without velocity information. A careful study of the behavior of these systems suggests that it is possible to balance a single pole with velocity information as an input and without learning to compute the velocity. A new fitness function is introduced that forces the neural network to compute the velocity. By using this new fitness function and tuning the syntactic constraints used with cellular encoding, we achieve a tenfold speedup over our previous study and solve a more difficult problem: balancing two poles when no information about the velocity is provided as input. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #650834. 	[Title] Neural Networks in an Artificial Life Perspective [Abstract] In the last few years several researchers within the Artificial Life and Mobile Robotics community used Artificial Neural Networks. Explicitly viewing Neural Networks in an Artificial Life perspective has a number of consequences that make research on what we will call Artificial Life Neural Networks ( ALNNs) rather different from traditional connectionist research. The aim of the paper is to make the differences between ALNNs and "classical" neural networks explicit. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #427606. 	[Title] Biological metaphors and the design of modular artificial neural networks Master's thesis of [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #650807. 	[Title] Discontinuity in evolution: how different levels of organization imply pre-adaptation [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #62329. 	[Title] Automatic Definition of Modular Neural Networks [Abstract]  Please summarise all the given information.

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.

Paper #1131565 has the title `GENE REGULATION AND BIOLOGICAL DEVELOPMENT IN NEURAL NETWORKS: AN EXPLORATORY MODEL  
` and the abstract `nan`.
Question: Which category should Paper #1131565 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1131565 has the title `GENE REGULATION AND BIOLOGICAL DEVELOPMENT IN NEURAL NETWORKS: AN EXPLORATORY MODEL  
` and the abstract `nan`.
Question: Which category should Paper #1131565 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: Neural_Networks