Paper Info
Title
Genetic programming over context-free languages with linear constraints for the knapsack problem: first results.
Abstract
In this paper, we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.
Year
DOI
Venue
2002
10.1162/106365602317301772
Evolutionary Computation
Keywords
DocType
Volume
grammatical evolution,genetic algorithms,combinatorial optimization
Journal
10
Issue
ISSN
Citations 
1
1063-6560
3
PageRank 
References 
Authors
0.49
19
2
Name
Order
Citations
PageRank
Peter Bruhn130.83
Andreas Geyer-Schulz213424.22