Evolutionary Computation in Combinatorial Optimization

Volume 6022 of the series Lecture Notes in Computer Science pp 59-70

A Genetic Algorithm to Minimize Chromatic Entropy

  • Greg DurrettAffiliated withComputer Science and Artificial Intelligence Laboratory
  • , Muriel MédardAffiliated withResearch Laboratory for Electronics, Massachusetts Institute of Technology
  • , Una-May O’ReillyAffiliated withComputer Science and Artificial Intelligence Laboratory

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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