Above are papers related to the following paper: [Paper_ID] Paper #1125597. 	[Title] Pruning backpropagation neural networks using modern stochastic optimization techniques [Abstract] Approaches combining genetic algorithms and neural networks have received a great deal of attention in recent years. As a result, much work has been reported in two major areas of neural network design: training and topology optimization. This paper focuses on the key issues associated with the problem of pruning a multilayer perceptron using genetic algorithms and simulated annealing. The study presented considers a number of aspects associated with network training that may alter the behavior of a stochastic topology optimizer. Enhancements are discussed that can improve topology searches. Simulation results for the two mentioned stochastic optimization methods applied to nonlinear system identification are presented and compared with a simple random search. 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 #513189. 	[Title] Extended Selection Mechanisms in Genetic Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82920. 	[Title] A Survey of Evolution Strategies [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #787016. 	[Title] Genetic Self-Learning [Abstract] Evolutionary Algorithms are direct random search algorithms which imitate the principles of natural evolution as a method to solve adaptation (learning) tasks in general. As such they have several features in common which can be observed on the genetic and phenotypic level of living species. In this paper the algorithms' capability of adaptation or learning in a wider sense is demonstrated, and it is focused on Genetic Algorithms to illustrate the learning process on the population level (first level learning), and on Evolution Strategies to demonstrate the learning process on the meta-level of strategy parameters (second level learning). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #573964. 	[Title] Optimization by Means of Genetic Algorithms [Abstract] Genetic Algorithms (GAs) are powerful heuristic search strategies based upon a simple model of organic evolution. The basic working scheme of GAs as developed by Holland [Hol75] is described within this paper in a formal way, and extensions based upon the second-level learning principle for strategy parameters as introduced in Evolution Strategies (ESs) are proposed. First experimental results concerning this extension of GAs are also reported. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #576691. 	[Title] Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first [Abstract] In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the search process. First experimental results are presented, which indicate that environment-dependent self-adaptation of appropriate settings for the mutation rate is possible even for GAs. Please summarise all the given information.

Paper #1125597 has the title `Pruning backpropagation neural networks using modern stochastic optimization techniques  
` and the abstract `Approaches combining genetic algorithms and neural networks have received a great deal of attention in recent years. As a result, much work has been reported in two major areas of neural network design: training and topology optimization. This paper focuses on the key issues associated with the problem of pruning a multilayer perceptron using genetic algorithms and simulated annealing. The study presented considers a number of aspects associated with network training that may alter the behavior of a stochastic topology optimizer. Enhancements are discussed that can improve topology searches. Simulation results for the two mentioned stochastic optimization methods applied to nonlinear system identification are presented and compared with a simple random search. 
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Question: Which category should Paper #1125597 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1125597 has the title `Pruning backpropagation neural networks using modern stochastic optimization techniques  
` and the abstract `Approaches combining genetic algorithms and neural networks have received a great deal of attention in recent years. As a result, much work has been reported in two major areas of neural network design: training and topology optimization. This paper focuses on the key issues associated with the problem of pruning a multilayer perceptron using genetic algorithms and simulated annealing. The study presented considers a number of aspects associated with network training that may alter the behavior of a stochastic topology optimizer. Enhancements are discussed that can improve topology searches. Simulation results for the two mentioned stochastic optimization methods applied to nonlinear system identification are presented and compared with a simple random search. 
`.
Question: Which category should Paper #1125597 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