Genetic Algorithms, Finance computation, Pattern matching, Symbolic Aggregate ApproXimation, GPU, CUDA",
abstract = "This paper starts by presenting a study from a computational performance standpoint of SAX/GA, an algorithm that uses the Symbolic Aggregate approXimation (SAX), to dimensionally reduce time series, and the Genetic Algorithm (GA) to optimise market trading strategies. This study highlights how the sequential implementation of SAX/GA and genetic operators works. This study is later used as the baseline for the development of parallel techniques capable of exploring the identified points of parallelism that simply focus on accelerating the heavy-duty fitness function to a full Graphical Processing Unit (GPU) accelerated GA. The implemented solutions accelerated the sequential single-core SAX/GA solution in about 30 times with a maximum of nearly 180 times.