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Architecturebased software performance optimization can not only significantly save time but also reduce cost. A few rulebased performance optimization approaches at software architecture (SA) level have been proposed in recent years. However, in these approaches, the number of rules being used and the order of application of each rule are uncertain in the optimization process and these uncertainties have not been fully considered so far. As a result, the search space for performance improvement is limited, possibly excluding optimal solutions. Aiming to solve this problem, we propose an evolutionary algorithm for rulebased performance optimization at SA level named EA4PO. First, the rulebased software performance optimization at SA level is abstracted into a mathematical model called RPOM. RPOM can precisely characterize the mathematical relation between the usage of rules and the optimal solution in the performance improvement space. Then, a framework named RSEF is designed to support the execution of rule sequences. Based on RPOM and RSEF, EA4PO is proposed to find the optimal performance improvement solution. In EA4PO, an adaptive mutation operator is designed to guide the search direction by fully considering heuristic information of rule usage during the evolution. Finally, the effectiveness of EA4PO is validated by comparing EA4PO with a typical rulebased approach. The results show that EA4PO can explore a relatively larger space and get better solutions. Keywords performance analysis; performance optimization algorithm; evolutionary algorithm; software architecture; search-based software engineering; rule=< , ⋯ , , ⋯ ,

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