NDLI: Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem

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Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem

Content Provider	ACM Digital Library
Author	Julstrom, Bryant A.
Abstract	Augmenting an evolutionary algorithm with knowledge of its target problem can yield a more effective algorithm, as this presentation illustrates. The Quadratic Knapsack Problem extends the familiar Knapsack Problem by assigning values not only to individual objects but also to pairs of objects. In these problems, an object's value density is the sum of the values associated with it divided by its weight. Two greedy heuristics for the quadratic problem examine objects for inclusion in the knapsack in descending order of their value densities. Two genetic algorithms encode candidate selections of objects as binary strings and generate only strings whose selections of objects have total weight no more than the knapsack's capacity. One GA is naive; its operators apply no information about the values associated with objects. The second extends the naive GA with greedy techniques from the non-evolutionary heuristics. Its operators examine objects for inclusion in the knapsack in orders determined by tournaments based on objects' value densities. All four algorithms are tested on twenty problem instances whose optimum knapsack values are known. The greedy heuristics do well, as does the naive GA, but the greedy GA exhibits the best performance. In repeated trials on the test instances, it identifies optimum solutions more than nine times out of every ten.
Starting Page	607
Ending Page	614
Page Count	8
File Format	PDF
ISBN	1595930108
DOI	10.1145/1068009.1068111
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-06-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Greedy operators Quadratic knapsack problem Genetic algorithms
Content Type	Text
Resource Type	Article

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