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GECCO
2005
Springer

Statistical analysis of heuristics for evolving sorting networks

10 years 5 months ago
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
Lee K. Graham, Hassan Masum, Franz Oppacher
Added 29 Jun 2010
Updated 29 Jun 2010
Type Conference
Year 2005
Where GECCO
Authors Lee K. Graham, Hassan Masum, Franz Oppacher
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