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by David M. Strong

Download Implementation and Analysis of the Parallel Genetic Rule and Classifier Construction Environment fb2, epub

ISBN: 1423528662
Author: David M. Strong
Language: English
Publisher: Storming Media (2001)
Subcategory: No category
Rating: 4.9
Votes: 199
Size Fb2: 1997 kb
Size ePub: 1652 kb
Size Djvu: 1161 kb
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A comparative analysis of the parallel versions of these techniques is. .In this paper, the parallel genetic algorithm PGA is applied to the optimization of continuous functions.

The PGA uses a mixed strategy. Subpopulations try to locate good local minima.

This paper presents a scalable parallel implementation of genetic programming on distributed memory machines. Two variants of the new crossover operator are described and tested on this landscapes. The system runs multiple master-slave instances each mapped on all the allocated nodes and multithreading is used to overlap message latencies with useful computation. Load balancing is achieved using a dynamic scheduling algorithm and comparison with a static algorithm is reported. Results show a reduction in the bloat phenomenon and in the frequency of deleterious crossovers.

In this paper, we present parallel implementations of the Gauss-Jordan and Gauss-Huard algorithms with scaled partial pivoting strategy on a cluster of Linux workstations using MPI as a parallel programming environment. We present a comparative study of their performance. We present a comparative study of their performance View. nepi-ng: An Efficient Experiment Control Tool in R2lab.

We describe a MIMD implementation of a parallel classifier which uses a.

We describe a MIMD implementation of a parallel classifier which uses a message-passing paradigm to effect interprocessor communications. Simulations and analysis of a local-area network implementation of the parallel classifier indicate that very large speedups may be obtained, and that speedups are limited only by the depth of the knowledge base. Preliminary results indicate that graph partitioning algorithms that cluster interdependent portions of the knowledge base may help to improve the efficiency of the parallel classifier.

Genetic algorithms and classifier systems: foundations and future directions. Genetic learning procedures in distributed environments. Parallel genetic algorithms for a hypercube. Adrian V. Sannier, II, Erik D. Goodman.

Discover Book Depository's huge selection of David Strong books online. Implementation and Analysis of the Parallel Genetic Rule and Classifier Construction Environment. Free delivery worldwide on over 20 million titles. Numerology Simplified.

Construction of the Tree Classifier Initial Tree Growing Methodology Methodological Development Two . Construction of trees from a learning sample. THE PURPOSES Of CLASSifiCATION ANALYSIS.

Construction of the Tree Classifier Initial Tree Growing Methodology Methodological Development Two Running Examples The Advantages of the Tree Structured Approach. Chapter 3. IltllltlJlJing on the problem, the basic purpose of a classification '1!:lIdy can be either to produce an accurate classifier or to. IIIll'OVOr the predictive structure of the problem.

Parallel construction is a device, which may be encountered not so much in the sentence. Pure parallel construction, however, does not depend on any other kind of repetition but the repetition of the syntactical design of the sentence. Parallel constructions may be partial or complete. The necessary condition in parallel construction is identical or, similar, syntactical structure in two or more, sentences or parts of a sentence, as in: .

The Genetic Rule and Classifier Construction Environment (GRaCCE) is a.Genetic optimization using a penalty function.

The Genetic Rule and Classifier Construction Environment (GRaCCE) is a proposed alternative to these approaches. Addison-Wesley, 1989. Chulhee Lee and David Landgrebe. Feature extraction based on decision boundaries. In Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA-93), 1993.

Genetic Programming Machine Code Symbolic Regression Linear Genetic Programming Technical Trading .

Genetic Programming Machine Code Symbolic Regression Linear Genetic Programming Technical Trading Rule. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING AND MANAGEMENT report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A920293. The abstract provided by the Pentagon follows: This paper discusses the Genetic Rule and Classifier Construction Environment (GRaCCE), which is an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm which uses evolutionary search to mine classification rules from data. The current implementation uses a genetic algorithm based 0/1 search to reduce the number of features to a minimal set of features that make the most significant contributions to the classification of the input data set. This feature selection increases the efficiency of the rule induction algorithm that follows. However, feature selection is shown to account for more than 98 percent of the total execution time of GRaCCE on the tested data sets. The primary objective of this research effort is to improve the overall performance of GRaCCE through the application of parallel computing methods to the feature selection algorithm. The development and implementation of a parallel feature selection algorithm is presented. The experiments designed and used to test this parallel implementation are outlined followed by an analysis of the results. The results of this thesis effort show clearly that GRaCCE is improved through the use of parallel programming techniques.

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