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The detection of tumors in digital images originated from mammograms can be a challenging task. In this paper we investigate a Computer Aided Diagnosis System based on a Genetic Programming classifier. The performance of the considered classifier is evaluated for five of the image descriptors used in literature and we propose a new approach, by utilizing descriptors fusion. Numerical experiments are performed on a sample of the Digital Database for Screening Mammography data set and indicate that descriptors fusion can lead to better classifying performances. [ABSTRACT FROM AUTHOR]

Recognition of people in images is important for many applications in computer vision. This paper presents an experimental study on pedestrian classification. We investigate the recently developed kernelbased features in order to represent an image and two learning algorithms: the popular Support Vector Machine (SVM) and Genetic Programming (GP). Numerical experiments are performed on a benchmark dataset consisting of pedestrian and non-pedestrian (labeled) images captured in outdoor urban environments and indicate that the evolutionary classifier is able to perform better over SVM. [ABSTRACT FROM AUTHOR]

Support Vector Machines ( SVMs) deliver state-of-the-art performance in real-world applications and are now established as one of the standard tools for machine learning and data mining. A key problem of these methods is how to choose an optimal kernel and how to optimise its parameters. The real-world applications have also emphasised the need to consider a combination of kernels-a multiple kernel-in order to boost the classification accuracy by adapting the kernel to the characteristics of heterogeneous data. This combination could be linear or non-linear, weighted or un-weighted. Several approaches have been already proposed to find a linear weighted kernel combination and to optimise its parameters together with the SVM parameters, but no approach has tried to optimise a non-linear weighted combination. Therefore, our goal is to automatically generate and adapt a kernel combination (linear or non-linear, weighted or un-weighted, according to the data) and to optimise both the kernel parameters and SVM parameters by evolutionary means in a unified framework. We will denote our combination as a kernel of kernels ( KoK). Numerical experiments show that the SVM algorithm, involving the evolutionary kernel of kernels ( eKoK) we propose, performs better than well-known classic kernels whose parameters were optimised and a state of the art convex linear and an evolutionary linear, respectively, kernel combinations. These results emphasise the fact that the SVM algorithm could require a non-linear weighted combination of kernels. [ABSTRACT FROM AUTHOR]

Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasized the need to consider a combination of kernels - also known as a multiple kernel (MK) - in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels. Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK - linear multiple kernels. These results emphasize the fact that the SVM algorithm requires a combination of kernels more complex than a linear one in order to boost its performance. [ABSTRACT FROM AUTHOR]

Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this research is to develop an autonomous system for solving data analysis problems. The system, called Genetic Programming-Autonomous Solver (GP-AS) contains most of the features required by an autonomous software: it decides if it knows or not how to solve a particular problem, it can construct solutions for new problems, it can store the created solutions for later use, it can improve the existing solutions in the idle-time it can efficiently manage the computer resources for fast running speed and it can detect and handle failure cases. The generator of solutions for new problems is based on an adaptive variant of Genetic Programming. We have tested this part by solving some well-known problems in the field of symbolic regression and classification. Numerical experiments show that the GP-AS system is able to perform very well on the considered test problems being able to successfully compete with standard GP having manually set parameters. [Copyright &y& Elsevier]