Program repair is an integral part of every software system's life-cycle but can be extremely challenging. To date, various automated program repair (APR) techniques have been proposed to reduce manual debugging efforts. However, given a real-world buggy program, a typical APR technique can generate a large number of patches, each of which needs to be validated against the original test suite, incurring extremely high computation costs. Although existing APR techniques have already leveraged various static and/or dynamic information to find the desired patches faster, they are still rather costly. In this work, we propose SeAPR (Self-Boosted Automated Program Repair), the first general-purpose technique to leverage the earlier patch execution information during APR to directly boost existing APR techniques themselves on-the-fly. Our basic intuition is that patches similar to earlier high-quality/low-quality patches should be promoted/degraded to speed up the detection of the desired patches. The experimental study on 13 state-of-the-art APR tools demonstrates that, overall, SeAPR can substantially reduce the number of patch executions with negligible overhead. Our study also investigates the impact of various configurations on SeAPR. Lastly, our study demonstrates that SeAPR can even leverage the historical patch execution information from other APR tools for the same buggy program to further boost the current APR tool.