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Finding Golomb rulers is an extremely challenging optimization problem (with many practical applications) that has been approached by a variety of search methods in recent years. This paper presents a hybrid evolutionary algorithm to find near-optimal Golomb rulers in reasonable time. The algorithm, which is conceptual simple and uses a natural modeling, focuses on feasibility, finding near-optimal rulers indirectly. It significantly outperforms earlier (hybrid) evolutionary algorithms and compares favorably with hybridizations of local search and constraint programming. In particular, the algorithm quickly finds optimal rulers with up to 11 marks and isolates optimal rulers with up to 14 marks in reasonable time. It also finds near-optimal rulers for up to 16 marks quickly.