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High-utility itemset mining (HUIM) is a major contemporary data mining issue. It is different from frequent itemset mining (FIM), which only considers the frequency factor. HUIM applies both the quantity and profit factors to be used to reveal the most profitable products. Several previous approaches have been proposed to mine high-utility itemsets (HUIs) and most of them have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of the database are both very large. Therefore, two evolutionary computation (EC) techniques, genetic algorithm (GA) and particle swarm optimization (PSO), were previously proposed to mine HUIs. In these studies, GAs and PSOs also could obtain the huge amount of high-utility items in a limitation time. In this paper, a novel algorithm based on the other evolutionary computation technique, ant colony optimization (ACO), is proposed to resolve this issue. Unlike GAs and PSOs, ACOs produce a feasible solution in a constructive way. They can avoid generating unreasonable solutions as much as possible. Thus, a well-defined ACO approach can always obtain suitable solutions efficiently. An ant colony system (ACS), which is extended from ACO and consists of high-utility itemset mining by ACS (HUIM-ACS), is proposed to efficiently find HUIs. In general, an EC algorithm cannot make sure the provided solution is the global optimal solution. But the designed HUIM-ACS algorithm maps the completed solution space into the routing graph and includes two pruning processes. Therefore, it guarantees that it obtains all of the HUIs when there is no candidate edge from the starting point. In addition, HUIM-ACS does not estimate the same feasible solution again in its process in order to avoid wasting computational resource. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of the number of discovered HUIs, and convergence. [ABSTRACT FROM AUTHOR]

Opening range breakout (ORB) is a well-known intraday trading strategy via technical analysis. ORB lacks robustness against market uncertainties (e.g., information from contradictory sources), and does not consider all relevant market characteristics. Furthermore, the closing strategies in generic ORB are not well defined. In this study, we developed an evolutionary ORB-based model, which utilized historical data to optimize thresholds in order to enhance profitability, and developed protective closing strategies aimed at to prevent unacceptable losses. Selecting appropriate thresholds and parameters for ORB is a non-trivial task, due to the fact that the search space exceeds sixty-five thousand options. We used evolutionary computation to derive rational strategies and parameters for ORB. The proposed framework based on a genetic algorithm optimizes the parameters related to threshold selection and protective closing strategies. In experiments, this resulted in annual returns of 9.3% (representing a 2.8% improvement over the original strategy) and Sharpe ratio of 2.5 (an improvement of 1.0), while reducing the maximum drawdown by half. The proposed scheme also reduced computational overhead by 89% compared to a grid search. [ABSTRACT FROM AUTHOR]