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This paper proposes a new strategy β-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm (GRA). Since the portfolio beta β efficiently measures the volatility relative to the benchmark index or the capital market, β is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process. The...
In this paper, a new evolutionary method named genetic relation algorithm (GRA) has been proposed and applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up a fixed number of the most efficient portfolio, the proposed...
Association rule mining is one of the tasks of data mining and it has been extensively studied recently. As a consequence, several methods for extracting association rules have been developed during the last years. Most of them use the support and confidence framework to extract the association rules. Researches are able to extract strong rules using this framework. However these measures are not...
The survey of the relevant literature showed that there have been many studies for portfolio optimization problem and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite high. But almost none of these studies deals with genetic relation algorithm (GRA). This study presents an approach to large-scale portfolio optimization problem using GRA...
One of the most important issues in any association rule mining is the interpretation and evaluation of discovered rules. Thus, most algorithms employ the support-confidence framework for evaluating association and classification rules. Unfortunately, recent studies show that the support and confidence measures are insufficient for filtering out uninteresting association rules, for instance, even...
In this paper, a novel evolutionary paradigm combining Genetic Network Programming (GNP) and Estimation of Distribution Algorithms (EDAs) is proposed and used to find important association rules in time-related applications, especially in traffic prediction. GNP is one of the evolutionary optimization algorithms, which uses directed-graph structures. EDAs is a novel algorithm, where the new population...
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