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Golomb Ruler Problem (GRP) is very hard combinatorial optimization problem which has received considerable attention over the last decades. Many different approaches have been proposed to tackle the Golomb Ruler Problem such as exact methods, constraint programming, local searches and evolutionary algorithms. In this paper, we proposed a new parallel evolutionary algorithm for Golomb Rulers. In addition, the paper analyzes fitness landscapes and presents an efficient incremental evaluation to evaluate neighbors. With this approach, we are capable of finding optimal and near-optimal rulers for up to 23 marks with a reasonable efficiency.