Statistical analysis of heuristics for evolving sorting networks

Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960’s. The application of evolutionary computing to this problem has yielded human-competitive results in recent years. We build on previous work by presenting a genetic algorithm whose parameters and heuristics are tuned on a small instance of the problem, and then scaled up to larger instances. Also presented are positive and negative results regarding the efficacy of several domain-specific heuristics. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning – parameter learning; I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Lee K. Graham, Hassan Masum, Franz Oppacher
• Keywords: Artificial Intelligence, Combinatorial Optimization Problem, Efficient Sorting Networks, GECCO 2005