A genetic algorithm to minimize chromatic entropy
Author(s)Durrett, Greg; Medard, Muriel; O'Reilly, Una-May
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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.
DepartmentMassachusetts 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
Proceedings of the 10th European Conference: Evolutionary Computation in Combinatorial Optimization, EvoCOP 2010, Istanbul, Turkey, April 7-9, 2010
Springer Science + Business Media B.V.
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.
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