A Genetic Algorithm to Minimize Chromatic Entropy

  • Greg Durrett
  • Muriel Médard
  • Una-May O’Reilly
Conference paper

DOI: 10.1007/978-3-642-12139-5_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6022)
Cite this paper as:
Durrett G., Médard M., O’Reilly UM. (2010) A Genetic Algorithm to Minimize Chromatic Entropy. In: Cowling P., Merz P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2010. Lecture Notes in Computer Science, vol 6022. Springer, Berlin, Heidelberg

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.

Keywords

chromatic entropy functional compression graph coloring 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Greg Durrett
    • 1
  • Muriel Médard
    • 2
  • Una-May O’Reilly
    • 1
  1. 1.Computer Science and Artificial Intelligence Laboratory 
  2. 2.Research Laboratory for ElectronicsMassachusetts Institute of Technology 

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