Above are papers related to the following paper: [Paper_ID] Paper #503871. 	[Title] Growing neural networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1033. 	[Title] The Evolutionary Cost of Learning [Abstract] Traits that are acquired by members of an evolving population during their lifetime, through adaptive processes such as learning, can become genetically specified in later generations. Thus there is a change in the level of learning in the population over evolutionary time. This paper explores the idea that as well as the benefits to be gained from learning, there may also be costs to be paid for the ability to learn. It is these costs that supply the selection pressure for the genetic assimilation of acquired traits. Two models are presented that attempt to illustrate this assertion. The first uses Kauffman's NK fitness landscapes to show the effect that both explicit and implicit costs have on the assimilation of learnt traits. A characteristic `hump' is observed in the graph of the level of plasticity in the population showing that learning is first selected for and then against as evolution progresses. The second model is a practical example in which neural network controllers are evolved for a small mobile robot. Results from this experiment also show the hump. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1035. 	[Title] EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY [Abstract] 1 . Sensors represent a crucial link between the evolutionary forces shaping a species' relationship with its environment, and the individual's cognitive abilities to behave and learn. We report on experiments using a new class of "latent energy environments" (LEE) models to define environments of carefully controlled complexity which allow us to state bounds for random and optimal behaviors that are independent of strategies for achieving the behaviors. Using LEE's analytic basis for defining environments, we then use neural networks (NNets) to model individuals and a steady - state genetic algorithm to model an evolutionary process shaping the NNets, in particular their sensors. Our experiments consider two types of "contact" and "ambient" sensors, and variants where the NNets are not allowed to learn, learn via error correction from internal prediction, and via reinforcement learning. We find that predictive learning, even when using a larger repertoire of the more sophisticated ambient sensors, provides no advantage over NNets unable to learn. However, reinforcement learning using a small number of crude contact sensors does provide a significant advantage. Our analysis of these results points to a tradeoff between the genetic "robustness" of sensors and their informativeness to a learning system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #240791. 	[Title] Auto-teaching: networks that develop their own teaching input [Abstract] Backpropagation learning (Rumelhart, Hinton and Williams, 1986) is a useful research tool but it has a number of undesiderable features such as having the experimenter decide from outside what should be learned. We describe a number of simulations of neural networks that internally generate their own teaching input. The networks generate the teaching input by trasforming the network input through connection weights that are evolved using a form of genetic algorithm. What results is an innate (evolved) capacity not to behave efficiently in an environment but to learn to behave efficiently. The analysis of what these networks evolve to learn shows some interesting results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #141342. 	[Title] Dynamic Parameter Encoding for Genetic Algorithms [Abstract] The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19621. 	[Title] References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature [Abstract] 2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Internal Representations by Error Propagation", in D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1), MIT Press (1986). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45605. 	[Title] Learning and evolution in neural networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #210871. 	[Title] Coevolving High-Level Representations [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144701. 	[Title] Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming [Abstract] An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to Genetic Programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using Genetic Programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations . An illustrative problem of 'gold' collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #682815. 	[Title] The Role of Development in Genetic Algorithms [Abstract] Technical Report Number CS94-394 Computer Science and Engineering, U.C.S.D. Abstract The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representational spaces are identical. We argue that a careful analysis of developmental mechanisms is useful when understanding the success of several standard GA techniques, and can clarify the relationships between more recently proposed enhancements. We provide a framework which distinguishes between two developmental mechanisms | learning and maturation | while also showing several common effects on GA search. This framework is used to analyze how maturation and local search can change the dynamics of the GA. We observe that in some contexts, maturation and local search can be incorporated into the fitness evaluation, but illustrate reasons for considering them seperately. Further, we identify contexts in which maturation and local search can be distinguished from the fitness evaluation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153860. 	[Title]  [Abstract] 1] R.K. Belew, J. McInerney, and N. Schraudolph, Evolving networks: using the genetic algorithm with connectionist learning, in Artificial Life II, SFI Studies in the Science of Complexity, C.G. Langton, C. Taylor, J.D. Farmer, S. Rasmussen Eds., vol. 10, Addison-Wesley, 1991. [2] M. McInerney, and A.P. Dhawan, Use of genetic algorithms with back propagation in training of feed-forward neural networks, in IEEE International Conference on Neural Networks, vol. 1, pp. 203-208, 1993. [3] F.Z. Brill, D.E. Brown, and W.N. Martin, Fast genetic selection of features for neural network classifiers, IEEE Transactions on Neural Networks, vol. 3, no. 2, pp. 324-328, 1992. [4] F. Dellaert, and J. Vandewalle, Automatic design of cellular neural networks by means of genetic algorithms: finding a feature detector, in The Third IEEE International Workshop on Cellular Neural Networks and Their Applications, IEEE, New Jersey, pp. 189-194, 1994. [5] D.E. Moriarty, and R. Miikkulainen, Efficient reinforcement learning through symbiotic evolution, Machine Learning, vol. 22, pp. 11-33, 1996. [6] L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991. [7] D. Whitely, The GENITOR algorithm and selective pressure, in Proceedings of the Third Interanational Conference on Genetic Algorithms, J.D. Schaffer Ed., Morgan Kauffman, San Mateo, CA, 1989, pp. 116-121. [8] van Camp, D., T. Plate and G.E. Hinton (1992). The Xerion Neural Network Simulator and Documentation. Department of Computer Science, University of Toronto, Toronto. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #263498. 	[Title] Automatic Design of Cellular Neural Networks by means of Genetic Algorithms: Finding a Feature Detector [Abstract] This paper aims to examine the use of genetic algorithms to optimize subsystems of cellular neural network architectures. The application at hand is character recognition: the aim is to evolve an optimal feature detector in order to aid a conventional classifier network to generalize across different fonts. To this end, a performance function and a genetic encoding for a feature detector are presented. An experiment is described where an optimal feature detector is indeed found by the genetic algorithm. We are interested in the application of cellular neural networks in computer vision. Genetic algorithms (GA's) [1-3] can serve to optimize the design of cellular neural networks. Although the design of the global architecture of the system could still be done by human insight, we propose that specific sub-modules of the system are best optimized using one or other optimization method. GAs are a good candidate to fulfill this optimization role, as they are well suited to problems where the objective function is a complex function of many parameters. The specific problem we want to investigate is one of character recognition. More specifically, we would like to use the GA to find optimal feature detectors to be used in the recognition of digits . Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #975567. 	[Title] Adaptive Behavior in Competing Co-Evolving Species [Abstract] Co-evolution of competitive species provides an interesting testbed to study the role of adaptive behavior because it provides unpredictable and dynamic environments. In this paper we experimentally investigate some arguments for the co-evolution of different adaptive protean behaviors in competing species of predators and preys. Both species are implemented as simulated mobile robots (Kheperas) with infrared proximity sensors, but the predator has an additional vision module whereas the prey has a maximum speed set to twice that of the predator. Different types of variability during life for neurocontrollers with the same architecture and genetic length are compared. It is shown that simple forms of pro-teanism affect co-evolutionary dynamics and that preys rather exploit noisy controllers to generate random trajectories, whereas predators benefit from directional-change controllers to improve pursuit behavior. 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 #32083. 	[Title] Back Propagation is Sensitive to Initial Conditions [Abstract] This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in weight space) is a very significant parameter in convergence time variability. In order to further understand this result, additional deterministic experiments were performed. The results of these experiments demonstrate the extreme sensitivity of back propagation to initial weight configuration. 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.

Above are papers related to the following paper: [Paper_ID] Paper #22229. 	[Title] The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action [Abstract] An essential component of an intelligent agent is the ability to notice, encode, store, and utilize information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, an approach to the automatic generation of agents that discover information about their environment, encode this information for later use, and create simple plans utilizing the stored mental models. In this approach, agents are multipart computer programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of 'gold' collection is used to demonstrate the approach in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the gold The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans. 1. Introduction Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113438. 	[Title] Modeling the Evolution of Motivation [Abstract] In order for learning to improve the adaptiveness of an animal's behavior and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions influence its reproductive fitness. For example, many circumstances that damage an animal, or otherwise reduce its fitness are painful and tend to be avoided. We refer to the mechanism by which an animal evaluates the fitness consequences of its actions as a "motivation system," and argue that such a system must evolve along with the behaviors it evaluates. We describe simulations of the evolution of populations of agents instantiating a number of different architectures for generating action and learning, in worlds of differing complexity. We find that in some cases, members of the populations evolve motivation systems that are accurate enough to direct learning so as to increase the fitness of the actions the agents perform. Furthermore, the motivation systems tend to incorporate systematic distortions in their representations of the worlds they inhabit; these distortions can increase the adaptiveness of the behavior generated. 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 #35061. 	[Title] Evolving Networks: Using the Genetic Algorithm with Connectionist Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131639. 	[Title] Language as a dynamical system [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152761. 	[Title]  [Abstract] 1] R.K. Belew, J. McInerney, and N. Schraudolph, Evolving networks: using the genetic algorithm with connectionist learning, in Artificial Life II, SFI Studies in the Science of Complexity, C.G. Langton, C. Taylor, J.D. Farmer, S. Rasmussen Eds., vol. 10, Addison-Wesley, 1991. [2] M. McInerney, and A.P. Dhawan, Use of genetic algorithms with back propagation in training of feed-forward neural networks, in IEEE International Conference on Neural Networks, vol. 1, pp. 203-208, 1993. [3] F.Z. Brill, D.E. Brown, and W.N. Martin, Fast genetic selection of features for neural network classifiers, IEEE Transactions on Neural Networks, vol. 3, no. 2, pp. 324-328, 1992. [4] F. Dellaert, and J. Vandewalle, Automatic design of cellular neural networks by means of genetic algorithms: finding a feature detector, in The Third IEEE International Workshop on Cellular Neural Networks and Their Applications, IEEE, New Jersey, pp. 189-194, 1994. [5] D.E. Moriarty, and R. Miikkulainen, Efficient reinforcement learning through symbiotic evolution, Machine Learning, vol. 22, pp. 11-33, 1996. [6] L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991. [7] D. Whitely, The GENITOR algorithm and selective pressure, in Proceedings of the Third Interanational Conference on Genetic Algorithms, J.D. Schaffer Ed., Morgan Kauffman, San Mateo, CA, 1989, pp. 116-121. [8] van Camp, D., T. Plate and G.E. Hinton (1992). The Xerion Neural Network Simulator and Documentation. Department of Computer Science, University of Toronto, Toronto. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #48766. 	[Title] Discovering Complex Othello Strategies Through Evolutionary Neural Networks [Abstract] An approach to develop new game playing strategies based on artificial evolution of neural networks is presented. Evolution was directed to discover strategies in Othello against a random-moving opponent and later against an ff-fi search program. The networks discovered first a standard positional strategy, and subsequently a mobility strategy, an advanced strategy rarely seen outside of tournaments. The latter discovery demonstrates how evolutionary neural networks can develop novel solutions by turning an initial disadvantage into an advantage in a changed environment. Please summarise all the given information.

Paper #503871 has the title `Growing neural networks  
` and the abstract `nan`.
Question: Which category should Paper #503871 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #503871 has the title `Growing neural networks  
` and the abstract `nan`.
Question: Which category should Paper #503871 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