A Genetic Algorithm to Minimize Chromatic Entropy

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.

• Type: Conference
• Year: 2010
• Where: EVOW
• Authors: Greg Durrett, Muriel Médard, Una-May O'Reilly
• Keywords: Artificial Intelligence, Chromatic Entropy, EVOW 2010, Genetic Algorithms, Graph Coloring