8 research outputs found
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithms
with real values that is based on the statistical theory of population
distributions. The operator is based on the theoretical distribution of the
values of the genes of the best individuals in the population. The proposed
operator takes into account the localization and dispersion features of the
best individuals of the population with the objective that these features would
be inherited by the offspring. Our aim is the optimization of the balance
between exploration and exploitation in the search process. In order to test
the efficiency and robustness of this crossover, we have used a set of
functions to be optimized with regard to different criteria, such as,
multimodality, separability, regularity and epistasis. With this set of
functions we can extract conclusions in function of the problem at hand. We
analyze the results using ANOVA and multiple comparison statistical tests. As
an example of how our crossover can be used to solve artificial intelligence
problems, we have applied the proposed model to the problem of obtaining the
weight of each network in a ensemble of neural networks. The results obtained
are above the performance of standard methods
Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Such weaknesses of the existing techniques have motivated the research presented in this thesis.
One of the weaknesses of the existing intrusion detection approaches is the usage of a raw dataset for classification but the classifier may get confused due to redundancy and hence may not classify correctly. To overcome this issue, Principal Component Analysis (PCA) has been employed to transform raw features into principal features space and select the features based on their sensitivity. The sensitivity is determined by the values of eigenvalues. The recent approaches use PCA to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power.
Based on the selected features, the classification is performed. The Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used for classification purpose due to their proven ability in classification. This research work uses the Knowledge Discovery and Data mining (KDD) cup dataset, which is considered benchmark for evaluating security detection mechanisms. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates
Pose-invariant, model-based object recognition, using linear combination of views and Bayesian statistics
This thesis presents an in-depth study on the problem of object recognition, and in particular the detection
of 3-D objects in 2-D intensity images which may be viewed from a variety of angles. A solution to this
problem remains elusive to this day, since it involves dealing with variations in geometry, photometry
and viewing angle, noise, occlusions and incomplete data. This work restricts its scope to a particular
kind of extrinsic variation; variation of the image due to changes in the viewpoint from which the object
is seen.
A technique is proposed and developed to address this problem, which falls into the category of
view-based approaches, that is, a method in which an object is represented as a collection of a small
number of 2-D views, as opposed to a generation of a full 3-D model. This technique is based on the
theoretical observation that the geometry of the set of possible images of an object undergoing 3-D rigid
transformations and scaling may, under most imaging conditions, be represented by a linear combination
of a small number of 2-D views of that object. It is therefore possible to synthesise a novel image of an
object given at least two existing and dissimilar views of the object, and a set of linear coefficients that
determine how these views are to be combined in order to synthesise the new image.
The method works in conjunction with a powerful optimization algorithm, to search and recover the
optimal linear combination coefficients that will synthesize a novel image, which is as similar as possible
to the target, scene view. If the similarity between the synthesized and the target images is above some
threshold, then an object is determined to be present in the scene and its location and pose are defined,
in part, by the coefficients. The key benefits of using this technique is that because it works directly
with pixel values, it avoids the need for problematic, low-level feature extraction and solution of the
correspondence problem. As a result, a linear combination of views (LCV) model is easy to construct
and use, since it only requires a small number of stored, 2-D views of the object in question, and the
selection of a few landmark points on the object, the process which is easily carried out during the offline,
model building stage. In addition, this method is general enough to be applied across a variety of
recognition problems and different types of objects.
The development and application of this method is initially explored looking at two-dimensional
problems, and then extending the same principles to 3-D. Additionally, the method is evaluated across
synthetic and real-image datasets, containing variations in the objectsâ identity and pose. Future work on
possible extensions to incorporate a foreground/background model and lighting variations of the pixels
are examined
Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Such weaknesses of the existing techniques have motivated the research presented in this thesis.
One of the weaknesses of the existing intrusion detection approaches is the usage of a raw dataset for classification but the classifier may get confused due to redundancy and hence may not classify correctly. To overcome this issue, Principal Component Analysis (PCA) has been employed to transform raw features into principal features space and select the features based on their sensitivity. The sensitivity is determined by the values of eigenvalues. The recent approaches use PCA to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power.
Based on the selected features, the classification is performed. The Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used for classification purpose due to their proven ability in classification. This research work uses the Knowledge Discovery and Data mining (KDD) cup dataset, which is considered benchmark for evaluating security detection mechanisms. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates