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It is known that a knapsack inequality can be reduced to one having the same solutions but with 'minimal' integer coefficients. Although this procedure is not practical because an exponential [...]

This paper examines privacy protection in a statistical database from the perspective of an intruder using learning theory to discover private information. With the rapid development of information technology, massive data collection is relatively easier and cheaper than ever before. The challenge is how to provide database users with reliable and useful data while protecting the privacy of the confidential information. This paper discusses how to prevent disclosing the identity of unique records in a statistical database. The authors ' research extends previous work and shows how much protection is necessary to prevent an adversary from discovering confidential data with high probability at small error. Keywords: Data Perturbation, Disclosure Control, Machine Learning, PAC Learning Theory, Privacy, Query, Statistical Database, INTRODUCTION Statistical organizations, such as the U.S. Census Bureau, collect large amounts of data every year and make it available to the public as statistical databases (SDBs). These organizations have [...]

Statistical learning theory provides a formal criterion for learning a concept from examples. This theory addresses directly the trade-off in empirical fit and generalization. In practice, this leads to the […]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.11.033 Byline: Haldun Aytug, Gary J. Koehler Keywords: Genetic algorithms; Markov chains; Worst-case analysis Abstract: Genetic algorithms are stochastic search algorithms that have been applied to optimization problems. In this paper we analyze the run-time complexity of a genetic algorithm when we are interested in one of a set of distinguished solutions. One such case occurs when multiple optima exist. We define the worst case scenario and derive a probabilistic worst case bound on the number of iterations required to find one of these multiple solutions of interest. Author Affiliation: Decision and Information Sciences, 351 BUS, The Warrington College of Business Administration, University of Florida, Gainesville, FL 32611, United States Article History: Received 27 April 2004; Accepted 30 November 2005

Roughly speaking, the no-free-lunch (NFL) theorems state that any blackbox algorithm has the same average performance as random search. These results have largely been ignored by algorithm researchers. This paper […]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.10.050 Byline: Parag C. Pendharkar (a), Gary J. Koehler (b) Keywords: Genetic algorithms; Heuristic search; Worst-case analysis; Error bounds; Markov processes Abstract: We propose two general stopping criteria for finite length, simple genetic algorithms based on steady state distributions, and empirically investigate the impact of mutation rate, string length, crossover rate and population size on their convergence. Our first stopping criterion is based on the second largest eigenvalue of the genetic algorithm transition matrix, and the second stopping criterion is based on minorization conditions. Author Affiliation: (a) Information Systems, School of Business Administration, Capital College, Pennsylvania State University, 777 West Harrisburg Pike, Middletown, PA 17057, United States (b) Decision and Information Sciences, 351 BUS, The Warrington College of Business Administration, University of Florida, Gainesville, FL 32611, United States Article History: Received 27 July 2004; Accepted 7 October 2005

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.04.045 Byline: Jackie Rees (a), Gary J. Koehler (b) Keywords: Artificial intelligence; Evolutionary computations; Evolutionary process; Adaptive agents; Genetic algorithms; Heuristics; Markov processes Abstract: Genetic algorithms (GAs) are routinely used to search problem spaces of interest. A lesser known but growing group of applications of GAs is the modeling of so-called 'evolutionary processes', for example, organizational learning and group decision-making. Given such an application, we show it is possible to compute the likely GA parameter settings given observed populations of such an evolutionary process. We examine the parameter estimation process using estimation procedures for learning hidden Markov models, with mathematical models that exactly capture expected GA behavior. We then explore the sampling distributions relevant to this estimation problem using an experimental approach. Author Affiliation: (a) Krannert Graduate School of Management, 403 West State Street, Purdue University, West Lafayette, IN 47907-2056, USA (b) Department of Decision and Information Sciences, Warrington College of Business, University of Florida, P.O. Box 117169, Gainesville FL 32611-7169, USA Article History: Received 7 October 2003; Accepted 14 April 2005