A genetic algorithm to minimize chromatic entropy
Name
durrett_oreilly_v5.pdf
Size
259.36 KB
Format
Adobe PDF
Checksum (MD5)
2a0cf9346d19a1e76b5fa375e9a32249
Author(s)
Durrett, Greg
Medard, Muriel
O'Reilly, Una-May
Date Issued
April 2010
Journal
Proceedings of the 10th European Conference: Evolutionary Computation in Combinatorial Optimization, EvoCOP 2010, Istanbul, Turkey, April 7-9, 2010
Publisher
Springer Science + Business Media B.V.
Citation
Durrett, Greg, Muriel Médard, and Una-May O’Reilly. “A Genetic Algorithm to Minimize Chromatic Entropy.” Evolutionary Computation in Combinatorial Optimization. Ed. Peter Cowling & Peter Merz. (Lecture notes in computer science, Vol. 6022). Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. 59–70.
Version
Author's final manuscript
Abstract
We present an algorithmic approach to solving the problem of chromatic entropy, a combinatorial optimization problem related to graph coloring. This problem is a component in algorithms for optimizing data compression when computing a function of two correlated sources at a receiver. Our genetic algorithm for minimizing chromatic entropy uses an order-based genome inspired by graph coloring genetic algorithms, as well as some problem-specific heuristics. It performs consistently well on synthetic instances, and for an expositional set of functional compression problems, the GA routinely finds a compression scheme that is 20-30% more efficient than that given by a reference compression algorithm.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-642-12139-5_6
