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Automatic programming is one of the challenging fields of AI to generate solutions for high-level programming problems. There are variant methodologies attempting to introduce an efficient technique which address problems of this domain. In this paper, a novel Rule-Centred Genetic Programming (RCGP) is proposed. RCGP benefits from a series of evolutionary rules to help the algorithm choose intelligent alterations in the chromosome of individuals during the evolution yet preserves its stochastic evolutionary nature. Further, a modified search strategy based on Imperialist Competitive Algorithm (ICA) is employed in RCGP that shows to be significantly effective to deal with various problems which differ in degree of complexity. The proposed method features competitive convergence both in the case of speed and accuracy as well as a simpler mechanism than the several existing GP methods. RCGP is tested on nine benchmark problems which are synthesis and real world. The obtained results indicate that RCGP outperforms recent GP methods and is capable of hybridizing with other types of evolutionary algorithms. The method shows to be competent enough to enhance the quality of automatic programming solutions in both aspects of accuracy and efficiency compared to existing methods. [ABSTRACT FROM AUTHOR]

Metamorphic malware is a kind of malware which evades signature-based anti-viruses by changing its internal structure in each infection. This paper, firstly, introduces a new measure of distance between two computer programs called program dissimilarity measure based on entropy (PDME). Then, it suggests a measure for the degree of metamorphism, based on the suggested distance measure. The distance measure is defined based on the Entropy of the two malware programs. Moreover, the paper shows that the distance measure can be used for classifying metamorphic malware via K-Nearest Neighbors (KNN) method. The method is evaluated by four metamorphic malware families. The results demonstrate that the measure can indicate the degree of metamorphism efficiently, and the KNN classification method using PDME can classify the metamorphic malware with a high precision. [ABSTRACT FROM AUTHOR]

This paper introduces a new method that improves the generalization ability of genetic programming (GP) for symbolic regression problems, named variance-based layered learning GP. In this approach, several datasets, called primitive training sets, are derived from the original training data. They are generated from less complex to more complex, for a suitable complexity measure. The last primitive dataset is still less complex than the original training set. The approach decomposes the evolution process into several hierarchical layers. The first layer of the evolution starts using the least complex (smoothest) primitive training set. In the next layers, more complex primitive sets are given to the GP engine. Finally, the original training data is given to the algorithm. We use the variance of the output values of a function as a measure of the functional complexity. This measure is utilized in order to generate smoother training data, and controlling the functional complexity of the solutions to reduce the overfitting. The experiments, conducted on four real-world and three artificial symbolic regression problems, demonstrate that the approach enhances the generalization ability of the GP, and reduces the complexity of the obtained solutions. [ABSTRACT FROM AUTHOR]

Mutual Information (MI) is an important dependency measure between random variables, due to its tight connection with information theory. It has numerous applications, both in theory and practice. However, when employed in practice, it is often necessary to estimate the MI from available data. There are several methods to approximate the MI, but arguably one of the simplest and most widespread techniques is the histogram-based approach. This paper suggests the use of fuzzy partitioning for the histogram-based MI estimation. It uses a general form of fuzzy membership functions, which includes the class of crisp membership functions as a special case. It is accordingly shown that the average absolute error of the fuzzy-histogram method is less than that of the naïve histogram method. Moreover, the accuracy of our technique is comparable, and in some cases superior to the accuracy of the Kernel density estimation (KDE) method, which is one of the best MI estimation methods. Furthermore, the computational cost of our technique is significantly less than that of the KDE. The new estimation method is investigated from different aspects, such as average error, bias and variance. Moreover, we explore the usefulness of the fuzzy-histogram MI estimator in a real-world bioinformatics application. Our experiments show that, in contrast to the naïve histogram MI estimator, the fuzzy-histogram MI estimator is able to reveal all dependencies between the gene-expression data. [ABSTRACT FROM AUTHOR]