Simple but efficient approaches for the collapsing knapsack problem
Discrete Applied Mathematics
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Computers and Operations Research
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Genetic Algorithms for the 0/1 Knapsack Problem
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Solving the 0/1 knapsack problem using an adaptive genetic algorithm
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Where are the hard knapsack problems?
Computers and Operations Research
Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Solving the multidimensional multiple-choice knapsack problem by constructing convex hulls
Computers and Operations Research
The quadratic multiple knapsack problem and three heuristic approaches to it
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Computers and Operations Research
Efficient tag detection in RFID systems
Journal of Parallel and Distributed Computing
Computers and Operations Research
A multi-level search strategy for the 0-1 Multidimensional Knapsack Problem
Discrete Applied Mathematics
Expert Systems with Applications: An International Journal
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Durable products and their components are increasingly being equipped with one of several forms of automatic identification technology such as radio frequency identification (RFID). This technology enables data collection, storage, and transmission of product information throughout its life cycle. Ideally all available relevant information could be stored on RFID tags with new information being added to the tags as it becomes available. However, because of the finite memory capacity of RFID tags along with the magnitude of potential lifecycle data, users need to be more selective in data allocation. In this research, the data allocation problem is modeled as a variant of the nonlinear knapsack problem. The objective is to determine the number of items to place on the tag such that the value of the ''unexplained'' data left off the tag is minimized. A binary encoded genetic algorithm is proposed and an extensive computational study is performed to illustrate the effectiveness of this approach. Additionally, we discuss some properties of the optimal solution which can be effective in solving more difficult problem instances.