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With increasing hardware capabilities and network capacity, applications operating on streams of data are becoming more prevalent in the computing industry. Used in areas from security such as packet-sniffing intrusion detection software packages to the financial world attempting to model the stock market to map out future trends, algorithms for processing these unbounded streams are growing in necessity. Traditional database management systems fall short, as they are limited to bounded data. Therefore, stream management systems are required, as well as algorithms to efficiently process these data streams. Furthermore, these algorithms must be agile, adaptive and suitable for a wide range of operating conditions. In this paper, we design a hybrid algorithm to find optimized join trees for continuous stream queries. Our experimental results show that this hybrid algorithm can generate more efficient join trees than its components under a wide range of varied conditions.