Above are papers related to the following paper: [Paper_ID] Paper #606479. 	[Title] The Evolution of Communication Schemes Over Continuous Channels [Abstract] Many problems impede the design of multi-agent systems, not the least of which is the passing of information between agents. While others hand implement communication routes and semantics, we explore a method by which communication can evolve. In the experiments described here, we model agents as connectionist networks. We supply each agent with a number of communications channels implemented by the addition of both input and output units for each channel. The output units initiate environmental signals whose amplitude decay over distance and are perturbed by environmental noise. An agent does not receive input from other individuals, rather the agents input reects the summation of all other agents output signals along that channel. Because we use real-valued activations, the agents communicate using real-valued vectors. Under our evolutionary program, GNARL, the agents coevolve a communication scheme over continuous channels which conveys task-spe cific information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #273152. 	[Title] Evolutionary Module Acquisition [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #44514. 	[Title] The Observers Paradox: Apparent Computational Complexity in Physical Systems [Abstract]  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 #1129610. 	[Title] Coevolving Communicative Behavior in a Linear Pursuer-Evader Game [Abstract] The pursuer-evader (PE) game is recognized as an important domain in which to study the coevolution of robust adaptive behavior and protean behavior (Miller and Cliff, 1994). Nevertheless, the potential of the game is largely unrealized due to methodological hurdles in coevolutionary simulation raised by PE; versions of the game that have optimal solutions (Isaacs, 1965) are closed-ended, while other formulations are opaque with respect to their solution space, for the lack of a rigorous metric of agent behavior. This inability to characterize behavior, in turn, obfuscates coevolutionary dynamics. We present a new formulation of PE that affords a rigorous measure of agent behavior and system dynamics. The game is moved from the two-dimensional plane to the one-dimensional bit-string; at each time step, the evader generates a bit that the pursuer must simultaneously predict. Because behavior is expressed as a time series, we can employ information theory to provide quantitative analysis of agent activity. Further, this version of PE opens vistas onto the communicative component of pursuit and evasion behavior, providing an open-ended serial communications channel and an open world (via coevolution). Results show that subtle changes to our game determine whether it is open-ended, and profoundly affect the viability of arms-race dynamics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593260. 	[Title] Evolving Graphs and Networks with Edge Encoding: Preliminary Report [Abstract] We present an alternative to the cellular encoding technique [Gruau 1992] for evolving graph and network structures via genetic programming. The new technique, called edge encoding, uses edge operators rather than the node operators of cellular encoding. While both cellular encoding and edge encoding can produce all possible graphs, the two encodings bias the genetic search process in different ways; each may therefore be most useful for a different set of problems. The problems for which these techniques may be used, and for which we think edge encoding may be particularly useful, include the evolution of recurrent neural networks, finite automata, and graph-based queries to symbolic knowledge bases. In this preliminary report we present a technical description of edge encoding and an initial comparison to cellular encoding. Experimental investigation of the relative merits of these encoding schemes is currently in progress. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #61069. 	[Title] Tracking the red queen: Measurements of adaptive progress in co-evolution ary simulations. In Third European [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #253971. 	[Title] Fools Gold: Extracting Finite State Machines From Recurrent Network Dynamics [Abstract] Several recurrent networks have been proposed as representations for the task of formal language learning. After training a recurrent network, the next step is to understand the information processing carried out by the network. Some researchers (Giles et al., 1992; Watrous & Kuhn, 1992; Cleeremans et al., 1989) have resorted to extracting finite state machines from the internal state trajectories of their recurrent networks. This paper describes two conditions, sensitivity to initial conditions and frivolous computational explanations due to discrete measurements (Kolen & Pollack, 1993), which allow these extraction methods to return illusionary finite state descriptions. 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 #210872. 	[Title] An Evolutionary Algorithm that Constructs Recurrent Neural Networks [Abstract] Standard methods for inducing both the structure and weight values of recurrent neural networks fit an assumed class of architectures to every task. This simplification is necessary because the interactions between network structure and function are not well understood. Evolutionary computation, which includes genetic algorithms and evolutionary programming, is a population-based search method that has shown promise in such complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. This algorithms empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38205. 	[Title] Competitive Environments Evolve Better Solutions for Complex Tasks [Abstract]  Please summarise all the given information.

Paper #606479 has the title `The Evolution of Communication Schemes Over Continuous Channels  
` and the abstract `Many problems impede the design of multi-agent systems, not the least of which is the passing of information between agents. While others hand implement communication routes and semantics, we explore a method by which communication can evolve. In the experiments described here, we model agents as connectionist networks. We supply each agent with a number of communications channels implemented by the addition of both input and output units for each channel. The output units initiate environmental signals whose amplitude decay over distance and are perturbed by environmental noise. An agent does not receive input from other individuals, rather the agents input reects the summation of all other agents output signals along that channel. Because we use real-valued activations, the agents communicate using real-valued vectors. Under our evolutionary program, GNARL, the agents coevolve a communication scheme over continuous channels which conveys task-spe cific information.
`.
Question: Which category should Paper #606479 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #606479 has the title `The Evolution of Communication Schemes Over Continuous Channels  
` and the abstract `Many problems impede the design of multi-agent systems, not the least of which is the passing of information between agents. While others hand implement communication routes and semantics, we explore a method by which communication can evolve. In the experiments described here, we model agents as connectionist networks. We supply each agent with a number of communications channels implemented by the addition of both input and output units for each channel. The output units initiate environmental signals whose amplitude decay over distance and are perturbed by environmental noise. An agent does not receive input from other individuals, rather the agents input reects the summation of all other agents output signals along that channel. Because we use real-valued activations, the agents communicate using real-valued vectors. Under our evolutionary program, GNARL, the agents coevolve a communication scheme over continuous channels which conveys task-spe cific information.
`.
Question: Which category should Paper #606479 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