Synthetic Optimization Problem Generation: Show Us the Correlations!
Abbreviated Journal Title
INFORMS J. Comput.
Keywords
random variable generation; correlation; 0-1 knapsack problem; generalized assignment problem; capital budgeting problem; set-covering; problem; 0-1 KNAPSACK-PROBLEM; GENERALIZED ASSIGNMENT PROBLEM; SINGLE-MACHINE; ALGORITHMS; PERFORMANCE; BOUNDS; Computer Science, Interdisciplinary Applications; Operations Research &; Management Science
Abstract
In many computational experiments, correlation is induced between certain types of coefficients in synthetic (or simulated) instances of classical optimization problems. Typically, the correlations that are induced are only qualified-that is, described by their presumed intensity. We quantify the population correlations induced under several strategies for simulating 0-1 knapsack problem instances and also for correlation-induction approaches used to simulate instances of the generalized assignment, capital budgeting (or multidimensional knapsack), and set-covering problems. We discuss implications of these correlation-induction methods for previous and future computational experiments on simulated optimization problems.
Journal Title
Informs Journal on Computing
Volume
21
Issue/Number
3
Publication Date
1-1-2009
Document Type
Article
Language
English
First Page
458
Last Page
467
WOS Identifier
ISSN
1091-9856
Recommended Citation
"Synthetic Optimization Problem Generation: Show Us the Correlations!" (2009). Faculty Bibliography 2000s. 2046.
https://stars.library.ucf.edu/facultybib2000/2046