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This paper analyzes and compares three popular evolution modeling methods in the structure, coding, genetic operations and performance. It then summarize their various aspects of characteristics, so as to enable researchers to improve the existing algorithms, and simultaneously facilitate the users to choose different algorithms. [ABSTRACT FROM AUTHOR]

In the paper we present a novel approach based on applying a modern metaheuristic Gene Expression Programming (GEP) to detecting web application attacks. This class of attacks relates to malicious activity of an intruder against applications, which use a database for storing data. The application uses SQL to retrieve data from the database and web server mechanisms to put them in a web browser. A poor implementation allows an attacker to modify SQL statements originally developed by a programmer, which leads to stealing or modifying data to which the attacker has not privileges. Intrusion detection problem is transformed into classification problem, which the objective is to classify SQL queries between either normal or malicious queries. GEP is used to find a function used for classification of SQL queries. Experimental results are presented on the basis of SQL queries of different length. The findings show that the efficiency of detecting SQL statements representing attacks depends on the length of SQL statements. [ABSTRACT FROM AUTHOR]

This is an application paper of applying standard methods of computational intelligence to identify gene diagnostic targets and to use them for a successful diagnosis of a medical problem - acute graft-versus-host disease (aGVHD). This is the major complication after allogeneic haematopoietic stem cell transplantation (HSCT) in which functional immune cells of donor recognize the recipient as ˵foreign″ and mount an immunologic attack. In this paper we analyzed gene-expression profiles of 47 genes associated with allo-reactivity in 59 patients submitted to HSCT. We have applied 2 feature selection algorithms combined with 2 different classifiers to detect the aGVHD at on-set of clinical signs. This is a preliminary study and the first paper which tackles both computational and biological evidence for the involvement of a limited number of genes for diagnosis of aGVHD. Directions for further studies are outlined. [ABSTRACT FROM AUTHOR]

DNA microarray allows the measurement of transcript abundances for thousands of genes in parallel. Though, it is an important procedure to select informative genes related to tumor from those gene expression profiles (GEP) because of its characteristics such as high dimensionality, small sample set and many noises. In this paper we proposed a novel method for feature extraction that is named as Orthogonal Discriminant Projection (ODP). This method is a linear approximation base on manifold learning approach. The ODP method characterizes the local and non-local information of manifold distributed data and explores an optimum subspace which can maximize the difference between non-local scatter and the local scatter. Moreover, it introduces the class information to enhance the recognition ability. A trick has been employed to handle the Small Sample Site (SSS). Experimental results on Non-small Cell Lung Cancer (NSCLC) and glioma dataset validates its efficiency compared to other widely used dimensionality reduction methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA). [ABSTRACT FROM AUTHOR]

There are several approaches to handle spatial trends: Kriging, stochastic simulation models, fractal and so on. The paper presents some contemporary approaches to spatial data analysis. The main topics are concentrated on the problems of space regression analysis by geochemical exploration data modeling. The innovative part of the paper presents integrated/hybrid model-combine GEP evolution modeling with spatial structure analysis. The models are based on GEP evolution modeling algorithm. Geostatistical tools on the basis of spatial autocorrelation thesis are used to extract representative data to fully utilize spatial structural information and weaken the influence of noise. Case study from mineral deposits in Gejiu illustrates the performance of the proposed model and BP neural network model is chosen as comparative study. It is shown that the fitting of the model and precision of test, provided by the combination of GEP evolution modeling and geostatistical model based approaches, are obviously improved. [ABSTRACT FROM AUTHOR]