A Genetic Algorithm to Minimize Chromatic Entropy

  • Greg Durrett
  • Muriel Médard
  • Una-May O’Reilly
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6022)


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.


chromatic entropy functional compression graph coloring 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Doshi, V., Shah, D., Medard, M., Jaggi, S.: Distributed functional compression through graph coloring. In: Data Compression Conference, DCC 2007, March 2007, pp. 93–102 (2007)Google Scholar
  2. 2.
    Cardinal, J., Fiorini, S., Van Assche, G.: A graph coloring problem with applications to data compression (2004)Google Scholar
  3. 3.
    Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19(4), 471–480 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Körner, J.: Coding of an information source having ambiguous alphabet and the entropy of graphs. In: 6th Prague Conference on Information Theory, pp. 411–425 (1973)Google Scholar
  5. 5.
    Alon, N., Orlitsky, A.: Source coding and graph entropies. IEEE Trans. Inform. Theory 42, 1329–1339 (1995)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Dorne, R., Hao, J.K.: A new genetic local search algorithm for graph coloring. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 745–754. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Eiben, A.E., Van Der Hauw, J.K., Van Hemert, J.I.: Graph coloring with adaptive evolutionary algorithms. Journal of Heuristics 4(1), 25–46 (1998)zbMATHCrossRefGoogle Scholar
  8. 8.
    Sivanandam, S.N., Sumathi, S., Hamsapriya, T.: A hybrid parallel genetic algorithm approach for graph coloring. Int. J. Know.-Based Intell. Eng. Syst. 9(3), 249–259 (2005)Google Scholar
  9. 9.
    Culberson, J.C., Luo, F.: Exploring the k-colorable landscape with iterated greedy. In: Dimacs Series in Discrete Mathematics and Theoretical Computer Science, pp. 245–284. American Mathematical Society, Providence (1995)Google Scholar

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 

Personalised recommendations