Testing effectiveness of genetic algorithms for exploratory data analysis

by Jason W. Carter

Publisher: Naval Postgraduate School, Publisher: Available from National Technical Information Service in Monterey, Calif, Springfield, Va

Written in English
Cover of: Testing effectiveness of genetic algorithms for exploratory data analysis | Jason W. Carter
Published: Pages: 65 Downloads: 376
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Subjects:

  • ALGORITHMS,
  • PERSIAN GULF WAR,
  • HEURISTIC METHODS,
  • GENETICS,
  • ILLNESS

About the Edition

Heuristic methods of solving exploratory data analysis problems suffer from one major weakness - uncertainty regarding the optimality of the results. The developers of DaMI (Data Mining Initiative), a genetic algorithm designed to mine the CCEP (Comprehensive Clinical Evaluation Program) database in the search for a Persian Gulf War syndrome, proposed a method to overcome this weakness: reproducibility -- the conjecture that consistent convergence on the same solutions is both necessary and sufficient to ensure a genetic algorithm has effectively searched an unknown solution space. We demonstrate the weakness of this conjecture in light of accepted genetic algorithm theory. We then test the conjecture by modifying the CCEP database with the insertion of an interesting solution of known quality and performing a discovery session using DaMI on this modified database. The necessity of reproducibility as a terminating condition is falsified by the algorithm finding the optimal solution without yielding strong reproducibility. The sufficiency of reproducibility as a terminating condition is analyzed by manual examination of the CCEP database in which strong reproducibility was experienced. Ex post facto knowledge of the solution space is used to prove that DaMI had not found the optimal solutions though it gave strong reproducibility, causing us to reject the conjecture that strong reproducibile is a sufficient terminating condition.

The Physical Object
Paginationx, 65 p. ;
Number of Pages65
ID Numbers
Open LibraryOL25303572M
OCLC/WorldCa640490282

Last M, Eyal S and Kandel A Effective black-box testing with genetic algorithms Proceedings of the First Haifa international conference on Hardware and Software Verification and Testing, () Herrera F and Lozano M () Two-Loop Real-Coded Genetic Algorithms . Tanagra data mining tool was used for exploratory data analysis, machine learning and statistical effective heart attack prediction using data mining. the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data . Sebastian Raschka 09/24/ Predictive modeling, supervised machine learning, and pattern classification - the big picture. When I was working on my next pattern classification . tion. This process of algorithm design, learning and test - ing is simultaneously analogous to the scientific method on two different levels. First, the design–learn–test process provides a principled way to test a hypothesis about machine learning: for example, algorithm File Size: KB.

10 Key Types of Data Analysis Methods and Techniques Our modern information age leads to a dynamic and extremely high growth of the data mining world. No doubt, that it requires adequate and effective different types of data analysis . This paper investigates the use of an evolutionary approach, called genetic algorithms, for test-suite reduction. The algorithm builds the initial population based on test history, calculates the fitness value using coverage and cost information, and then selectively breeds the successive generations using genetic Cited by: Need for Genetic Algorithm Testing: . The test engineer develop the test case or test data for the software where they analyses the quality of software all the test cases are updated manually which consume lot of time for upgrading, also the requirement of mannul test . Genetic Algorithms PDF Download. Have you ever read Genetic Algorithms PDF Download e-book? Not yet? Well, you must try it. As known, reading a Genetic Algorithms PDF ePub is a .

Thesis: "Testing Effectiveness of Genetic Algorithms for Exploratory Data Analysis" Missouri University of Science and Technology B.S. Metallurgical EngineeringTitle: Founder/President, UNCOMN. Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial e learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.: 2 Machine learning algorithms . In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm Cited by:: Cited by::

Testing effectiveness of genetic algorithms for exploratory data analysis by Jason W. Carter Download PDF EPUB FB2

The developers of DaMI (Data Mining Initiative), a genetic algorithm designed to mine the CCEP (Comprehensive Clinical Evaluation Program) database in the search for a Persian Gulf War syndrome, proposed a method to overcome this weakness: reproducibility -- the conjecture that consistent convergence on the same solutions is both necessary and sufficient to ensure a genetic algorithm Pages: In the present work, we propose a new data projection method that uses genetic algorithms to find linear Data projection is a commonly used technique applied to analyse high dimensional data.

Genetic Algorithms for Exploratory Data Analysis Author: Alberto Perez-Jimenez, Juan Carlos Pérez-Cortes. Statistical Exploratory Analysis of Genetic Algorithms Genetic algorithms (GAs) have been extensively used and studied in com- puter science, yet there is no generally accepted.

Statistical methods that analyze how the interactions between the variables of the problem inuence the behavior of the EAs [26]or exploratory landscape analysis [18,33]that try to discern how the tness landscapes of the functions inuence the performance of EAs, are two other approaches to study the behavior of these algorithms.

Abstract Inthis paper, statistical analysis, in the form of regression models and fractional factorial experiments, has been applied to genetic algorithms (GA) ‐ a search method for. Automatic test data generation using genetic algorithm and program dependence graphs.

Abstract. The complexity of software systems has been increasing dramatically in the past decade, and software testing Cited by: adaptive nature of the Genetic Algorithms (GA). They are represented by chromosome like data structure which uses recursive recombination or search techniques.

Let u be a set of sample points, u = {u. 1,u. 2,u. 3.,u. Then from a genetic algorithm. To this aim exploratory data analysis (EDA) is well suited. EDA is well known in statistics and sciences as that operative approach to data analysis aimed to improve understanding and Cited by: This practical introduces basic multivariate analysis of genetic data using the adegenet and ade4 packages for the R software.

We brie y show how genetic marker data can be read into R and how they are stored in adegenet, and then introduce basic population genetics analysis File Size: KB. The results showed that genetic algorithms have been successfully applied to simple test data generation, but are rarely used to generate complex test data such as images, videos.

This paper describes experiments designed to ascertain the effectiveness of Genetic Algorithms as optimizers for exploratory projection pursuit. GADGET, Michael, applies genetic algorithms (GA) to the test data generation specifically targets condition-decision coverage as its testing objective.

Cited by: Depending on the type of analysis, the number of prototypes, and the accuracy with which the prototypes represent the data, the results can be comparable to those that would have been obtained if all the data could have been used.

• Compression. Cluster prototypes can also be used for data compres. In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract.

Zbigniew Michalewicz's Genetic Algorithms + Data Structures = Evolution Programs has three sections. The first section is a straightforward introduction to genetic algorithms. In the second Cited by: An introduction to genetic algorithms / Melanie Mitchell.

"A Bradford book." Includes bibliographical references and index. ISBN 0−−−4 (HB), 0−−−7 (PB) 1. Abstract. In a medical imaging system, uncertainties can be present at any point resulting from incomplete or imprecise input information, ambiguity in medical images, ill-defined or.

The data extraction and synthesis process and the taxonomic classification are used to answer the research questions.

We also performed a rigorous analysis of the techniques by comparing them on research methodology, the prioritization method, dataset specification, test suite size, types of genetic algorithms Cited by: 3.

In "Genetic Data Analysis" a full account of the methodology appropriate for count data is presented. Starting with the basic idea of estimating gene frequencies, and proceeding through a wide range of topics to building phyilogenetic trees, the book contains the tools for analyzing genetic data Cited by: Optimizing the software testing efficiency by using a genetic algorithm: a design methodology.

Share on. Mark Last, Shay Eyal, "Effective Black-Box Testing with Genetic Algorithms" Department of Information Systems Engineering, Optimizing the software testing efficiency by using a genetic : RaoK.

Koteswara, RajuGSVP, NagarajSrinivasan. Industrial Applications of Genetic Algorithms shows how GAs have made the leap form their origins in the laboratory to the practicing engineer's toolbox.

Each chapter in the book Format: Hardcover. algorithms in data mining techniques was explained and detailed. Joshi et al. () studied on the use of different genetic algorithms in the field of data mining and knowledge discovery. The main reason for the use of genetic algorithm technique in data File Size: KB.

Statistical arbitrage on the KOSPI An exploratory analysis of classification and prediction machine learning algorithms for day trading Ian Sutherland • Yesuk Jung* • Gunhee Lee File Size: KB.

• Significant experience analyzing textual and numerical data, including mining information from large corpora (over million lines of code in 9, Java programs) & applying a variety of learning techniques: statistics (logistic regression), machine learning (decision trees, genetic algorithms), and human learning (exploratory data analysis).

As the number of genetic tests has expanded rapidly over the last decade, so have the different types of genetic testing methodologies used.

The type of test employed depends on the type of abnormality being measured. In general, three categories of genetic testing Author: Newborn ScreeningServices.

Exploratory Data Analysis for Complex Models to genetic algorithms to Markov chain simulation allow users to fit models that have no More generally, complex modeling makes exploratory analysis more effective in the sense of being able to capture more subtle patterns in data.

The genetic algorithm. A GA is a metaheuristic method, inspired by the laws of genetics, trying to find useful solutions to complex problems. In this method, first some random Cited by: In this paper we propose a genetic algorithm for co-evolving four important aspects of exploratory multivariate time-series analysis: (i) a subset of features to be used as input into some sort of Cited by: 1.

Data reduction methods such as the multifactor dimensionality reduction or MDR approach (Ritchie et al., ) seek to reduce the dimensionality of the problem in order to facilitate exploratory data analysis and hypothesis testing Cited by:   The amazing Genetic Algorithms.

Posted: 31 January, Use new generated population for a further run of algorithm [Test] If the end condition is satisfied, stop, and return the best solution in current population provide a link between spatial analysis and graphing packages such that the user can be quickly and easily manipulate data.

Data analysis: Data Analysis is the process of inspecting, cleaning and modelling data with the objective of discovering useful information, arriving at conclusion Statistics: Statistical .The classical neural network model is used for prediction, on the pre-processed dataset.

In predictive analysis of diabetic treatment using regression based data mining techniques to Cited by: A test that the algorithm gives you the same result for the same input could help you but sometimes you will make changes that change the result picking behavior of the algorithm.

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