Above are papers related to the following paper: [Paper_ID] Paper #1121398. 	[Title] Overview of Selection Schemes and a Suggested Classification [Abstract] In this paper we emphasize the role of selection in evolutionary algorithms. We briefly review some of the most common selection schemes from the fields of Genetic Algorithms, Evolution Strategies and Genetic Programming. However we do not classify selection schemes according to which group of evolutionary algorithm they belong to, but rather distinguish between parent selection schemes, global competition and replacement schemes, and local competition and replacement schemes. This paper does not intend to fully review and analyse each of the presented selection schemes but tries to be a short reference for standard and some more advanced selection schemes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #579008. 	[Title] Genetic Programming and Redundancy [Abstract] The Genetic Programming optimization method (GP) elaborated by John Koza [ Koza, 1992 ] is a variant of Genetic Algorithms. The search space of the problem domain consists of computer programs represented as parse trees, and the crossover operator is realized by an exchange of subtrees. Empirical analyses show that large parts of those trees are never used or evaluated which means that these parts of the trees are irrelevant for the solution or redundant. This paper is concerned with the identification of the redundancy occuring in GP. It starts with a mathematical description of the behavior of GP and the conclusions drawn from that description among others explain the "size problem" which denotes the phenomenon that the average size of trees in the population grows with time. 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 #1113831. 	[Title] Determining Successful Negotiation Strategies: An Evolutionary Approach [Abstract] To be successful in open, multi-agent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the relative success of different strategies against different types of opponent in different environments. In particular, we adopt an evolutionary approach in which strategies and tactics correspond to the genetic material in a genetic algorithm. We conduct a series of experiments to determine the most successful strategies and to see how and when these strategies evolve depending on the context and negotiation stance of the agent's opponent. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #248425. 	[Title] A Comparison of Selection Schemes used in Genetic Algorithms [Abstract] TIK-Report Nr. 11, December 1995 Version 2 (2. Edition) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592975. 	[Title] Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP [Abstract] This paper presents Limited Error Fitness (LEF), a modification to the standard supervised learning approach in Genetic Programming (GP), in which an individual's fitness score is based on how many cases remain uncovered in the ordered training set after the individual exceeds an error limit. The training set order and the error limit are both altered dynamically in response to the performance of the fittest individual in the previous generation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594387. 	[Title] Evolving Compact Solutions in Genetic Programming: A Case Study [Abstract] Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y = g(x) that approximates a set of data points (x i ; y i ). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data. Please summarise all the given information.

Paper #1121398 has the title `Overview of Selection Schemes and a Suggested Classification  
` and the abstract `In this paper we emphasize the role of selection in evolutionary algorithms. We briefly review some of the most common selection schemes from the fields of Genetic Algorithms, Evolution Strategies and Genetic Programming. However we do not classify selection schemes according to which group of evolutionary algorithm they belong to, but rather distinguish between parent selection schemes, global competition and replacement schemes, and local competition and replacement schemes. This paper does not intend to fully review and analyse each of the presented selection schemes but tries to be a short reference for standard and some more advanced selection schemes. 
`.
Question: Which category should Paper #1121398 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1121398 has the title `Overview of Selection Schemes and a Suggested Classification  
` and the abstract `In this paper we emphasize the role of selection in evolutionary algorithms. We briefly review some of the most common selection schemes from the fields of Genetic Algorithms, Evolution Strategies and Genetic Programming. However we do not classify selection schemes according to which group of evolutionary algorithm they belong to, but rather distinguish between parent selection schemes, global competition and replacement schemes, and local competition and replacement schemes. This paper does not intend to fully review and analyse each of the presented selection schemes but tries to be a short reference for standard and some more advanced selection schemes. 
`.
Question: Which category should Paper #1121398 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