Learn More
Stock price predicting is an important concern for investors, who by using high accuracy prediction systems are able to make a great profit. In recent years, artificial neural networks (ANNs) have shown promising results in this area, and have been improved in many ways. However, there are still some issues with ANN that remain unanswered, one of which is(More)
Many heuristic methods or evolutionary algorithms such as Genetic Algorithm (GA) and Genetic Programming (GP) are common approaches used in financial applications. Determining the best time to buy and sell in a stock market, and thereby maximizing the profit with lower risks are important issues in financial research. Recent researches have used trading(More)
In this paper, we propose a novel method named Quantum-inspired Tabu Search (QTS) algorithm for applying to a trading system. Determining the best time to buy and sell in a stock market and thereby maximizing the profit with lower risks are important issues in financial research. In order to find ideal trading points, the proposed trading system use(More)
After we read the paper about quantum-inspired tabu search algorithm (QTS) for solving 0/1 knapsack problems [5], we got many ideas. In this study, we proposed a method which is called improved quantum-inspired tabu search algorithm (IMQTS). In IMQTS, we add two skills in QTS. First, we add the probability of taking a worse solution become the guide of(More)
Heuristic methods or evolutionary algorithms (such as genetic algorithms and genetic programs) are common approaches applied in financial applications, such as trading systems. Determining the best time to buy or sell stocks in a stock market, and thereby maximizing profit with low risks, is an important issue in financial research. Recent studies have used(More)
Recently evolutionary algorithms, such as the Genetic Algorithm (GA), Genetic Programming (GP) and Particle Swarm Optimization (PSO), have become common approaches used in financial applications to address stock trading problems. In this paper, we propose a novel method called the Multi-objective Quantum-inspired Tabu Search (MOQTS) algorithm, which can be(More)
The most powerful metaheuristics must be good at both exploitation and exploration. In the present day, metaheuristics are designed to reach a balance between these two capabilities for the sake of avoiding being trapped in the local optimum or unable to achieve convergence. For the first time in history, it is noteworthy that exploitation and exploration(More)
Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have high dependence, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional(More)
Reversible logic plays an important role in quantum computation, which is a promising research field. The reversible logic synthesis problem focuses on generating a reversible circuit automatically and finding the lowest cost when an output function is given. The synthesis of reversible logic circuits can be formulated as a combinatorial optimization(More)