Above are papers related to the following paper: [Paper_ID] Paper #1153784. 	[Title]  [Abstract] dvitps ERROR: reno98b.dvi @ puccini.rutgers.edu Certain fonts that you requested in your dvi file could not be found on the system. In order to print your document, other fonts that are installed were substituted for these missing fonts. Below is a list of the substitutions that were made. /usr/local/lib/fonts/gf/cmbx12.518pk substituted for cmbx12.519pk 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 #1128453. 	[Title] An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization [Abstract] In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #801170. 	[Title] Guided Crossover: A New Operator for Genetic Algorithm Based Optimization [Abstract] Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a tendency of getting only moderately close to the optima in a small number of iterations. To get very close to the optima, the GA needs a very large number of iterations. Whereas gradient based optimizers usually get very close to local optima in a relatively small number of iterations. In this paper we describe a new crossover operator which is designed to endow the GA with gradient-like abilities without actually computing any gradients and without sacrificing global optimality. The operator works by using guidance from all members of the GA population to select a direction for exploration. Empirical results in two engineering design domains and across both binary and floating point representations demonstrate that the operator can significantly improve the steady state error of the GA optimizer. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #289779. 	[Title] Adaptation of Genetic Algorithms for Engineering Design Optimization [Abstract] Genetic algorithms have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains it was observed that a simple classical implementation of the GA based on binary encoding and bit mutation and crossover was sometimes inefficient and unable to reach the global optimum. Using floating point representation alone does not eliminate the problem. In this paper we describe a way of augmenting the GA with new operators and strategies that take advantage of the structure and properties of such engineering design domains. Empirical results (initially in the domain of conceptual design of supersonic transport aircraft and the domain of high performance supersonic missile inlet design) demonstrate that the newly formulated GA can be significantly better than the classical GA in terms of efficiency and reliability. http://www.cs.rutgers.edu/~shehata/papers.html Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #289780. 	[Title] Learning to be Selective in Genetic-Algorithm-Based Design Optimization [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #289781. 	[Title] A Genetic Algorithm for Continuous Design Space Search [Abstract] Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains the simple, classical implementation of a GA based on binary encoding and bit mutation and crossover is often inefficient and unable to reach the global optimum. In this paper we describe a GA for continuous design-space optimization that uses new GA operators and strategies tailored to the structure and properties of engineering design domains. Empirical results in the domains of supersonic transport aircraft and supersonic missile inlets demonstrate that the newly formulated GA can be significantly better than the classical GA in both efficiency and reliability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #87417. 	[Title] Using Modeling Knowledge to Guide Design Space Search [Abstract] Automated search of a space of candidate designs seems an attractive way to improve the traditional engineering design process. To make this approach work, however, the automated design system must include both knowledge of the modeling limitations of the method used to evaluate candidate designs and also an effective way to use this knowledge to influence the search process. We suggest that a productive approach is to include this knowledge by implementing a set of model constraint functions which measure how much each modeling assumptions is violated, and to influence the search by using the values of these model constraint functions as constraint inputs to a standard constrained nonlinear optimization numerical method. We test this idea in the domain of conceptual design of supersonic transport aircraft, and our experiments indicate that our model constraint communication strategy can decrease the cost of design space search by one or more orders of magnitude. Please summarise all the given information.

Paper #1153784 has the title `nan` and the abstract `dvitps ERROR: reno98b.dvi @ puccini.rutgers.edu Certain fonts that you requested in your dvi file could not be found on the system. In order to print your document, other fonts that are installed were substituted for these missing fonts. Below is a list of the substitutions that were made. /usr/local/lib/fonts/gf/cmbx12.518pk substituted for cmbx12.519pk 
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Question: Which category should Paper #1153784 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1153784 has the title `nan` and the abstract `dvitps ERROR: reno98b.dvi @ puccini.rutgers.edu Certain fonts that you requested in your dvi file could not be found on the system. In order to print your document, other fonts that are installed were substituted for these missing fonts. Below is a list of the substitutions that were made. /usr/local/lib/fonts/gf/cmbx12.518pk substituted for cmbx12.519pk 
`.
Question: Which category should Paper #1153784 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