Accession Number : ADA333989

Title :   Testing Effectiveness of Genetic Algorithms for Exploratory Data Analysis.

Descriptive Note : Master's thesis,

Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s) : Carter, Jason W.

PDF Url : ADA333989

Report Date : SEP 1997

Pagination or Media Count : 76

Abstract : 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.

Descriptors :   *ALGORITHMS, *ILLNESS, *HEURISTIC METHODS, *GENETICS, *PERSIAN GULF WAR, DATA PROCESSING, EXPERIMENTAL DESIGN, CLINICAL MEDICINE, SIGNS AND SYMPTOMS, SOLUTIONS(GENERAL), REPRODUCIBILITY, WEAK CONVERGENCE.

Subject Categories : Genetic Engineering and Molecular Biology
      Medicine and Medical Research
      Geography

Distribution Statement : APPROVED FOR PUBLIC RELEASE