A genetic algorithm to minimize chromatic entropy

Authors:
Greg Durrett;Muriel Médard;Una-May O'Reilly
Affiliations:
Computer Science and Artificial Intelligence Laboratory;Research Laboratory for Electronics, Massachusetts Institute of Technology;Computer Science and Artificial Intelligence Laboratory
Venue:
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2010

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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.