Gary J. Koehler

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Genetic Algorithms have been successfully applied in a wide variety of problems. Although widely used, there are few theoretical guidelines for determining when to terminate the search. One result by Aytug and Koehler provides a loose bound on the number of GA generations needed to see all populations (and hence, an optimal solution) with a specified(More)
T his paper provides a methodology for detecting management fraud using basic financial data. The methodology is based on support vector machines. An important aspect therein is a kernel that increases the power of the learning machine by allowing an implicit and generally nonlinear mapping of points, usually into a higher dimensional feature space. A(More)
Single-item auctions have many desirable properties. Mechanisms exist to ensure optimality, incentive compatibility and market-clearing prices. When multiple items are offered through individual auctions, a bidder wanting a bundle of items faces an exposure problem if the bidder places a high value on a combination of goods but a low value on strict subsets(More)
We explore data-driven methods for gaining insight into the dynamics of a two-population genetic algorithm (GA), which has been effective in tests on constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solutions are selected and bred to improve their objective(More)