Victor Ciesielski

Representing classification problems in genetic programming

Five alternative methods are proposed to perform multi-class classification tasks using genetic programming. These methods are: (1) binary decomposition, in which the problem is decomposed into a set of binary problems and standard genetic programming methods are applied; (2) static range selection, where the set of real values returned by a genetic program is divided into class boundaries using arbitrarily-chosen division points; (3) dynamic range selection, in which a subset of training examples are used to determine where, over the set of reals, class boundaries lie; (4) class enumeration, which constructs programs similar in syntactic structure to a decision tree; and (5) evidence accumulation, which allows separate branches of the program to add to the certainty of any given class. The results show that the dynamic range selection method is well-suited to the task of multi-class classification and is capable of producing classifiers that are more accurate than the other methods tried when comparable training times are allowed. The accuracy of the generated classifiers was comparable to alternative approaches over several data sets.

A domain-independentwindow approach to multiclass object detection using genetic programming

This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.

Genetic Programming for Multiple Class Object Detection

We describe an approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large pictures must be found. The evolved programs use a feature set computed from a square input field large enough to contain each of objects of interest and are applied, in moving window fashion, over the large pictures in order to locate the objects of interest. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty with four different classes of interest. On pictures of easy and medium difficulty all objects axe detected with no false alarms. On difficult pictures there are still significant numbers of errors, however the results are considerably better than those of a neural network based program for the same problems.

Multi-objective techniques in genetic programming for evolving classifiers

The application of multi-objective evolutionary computation techniques to the genetic programming of classifiers has the potential to both improve the accuracy and decrease the training time of the classifiers. The performance of two such algorithms is investigated on the even 6-parity problem and the Wisconsin breast cancer, Iris and Wine data sets from the UCI repository. The first method explores the addition of an explicit size objective as a parsimony enforcement technique. The second represents a programs classification accuracy on each class as a separate objective. Both techniques give a lower error rate with less computational cost than was achieved using a standard GP with the same parameters.

Towards Genetic Programming for Texture Classification

The genetic programming (GP) method is proposed as a new approach to perform texture classification based directly on raw pixel data. Two alternative genetic programming representations are used to perform classification. These are dynamic range selection (DRS) and static range selection (SRS). This preliminary study uses four brodatz textures to investigate the applicability of the genetic programming method for binary texture classifications and multi-texture classifications.Results indicate that the genetic programming method, based directly on raw pixel data, is able to accurately classify different textures. The results show that the DRS method is well suited to the task of texture classification. The classifiers generated in our experiments by DRS have good performance over a variety of texture data and offer GP as a promising alternative approach for the difficult problem of texture classification.

Texture classifiers generated by genetic programming

We investigate the behaviour of image texture classifiers generated by genetic programming. We propose techniques to understand how classifiers capture textural characteristics and for discussing the effectiveness of different classifiers. Our results show that regularities of patterns can be detected by the genetic programming method without predefined knowledge.

Texture segmentation by genetic programming

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming

In this paper we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level his- tograms of different textures are used as inputs to the evolved programs. One dimensional K-means clustering is applied to the outputs and the tightness of the clusters is used as the fitness measure. To test general- ity, textures from the Brodatz library were used in learning phase and the evolved features were used on classification problems based on the Vistex library. Using the evolved features gave a test accuracy of 74.8% while using Haralick features, the most commonly used method in tex- ture classification, gave an accuracy of 75.5% on the same problem. Thus, the evolved features are competitive with those derived by human intu- ition and analysis. Furthermore, when the evolved features are combined with the Haralick features the accuracy increases to 83.2%, indicating that the evolved features are finding texture regularities not used in the Haralick approach.

Texture analysis by genetic programming

This work presents the use of genetic programming (GP) to a complex domain, texture analysis. Two major tasks of texture analysis, texture classification and texture segmentation, are studied. Bitmap textures are used in this investigation. In classification tasks, the results show that GP is able to evolve accurate classifiers based on texture features. Moreover by using the presented method, GP is able to evolve accurate classifiers without extracting texture features. In texture segmentation tasks, the investigation shows that a fast and accurate segmentation method can be developed based on GP generated texture classifiers. Our further investigation show that the accuracies are not achieved by chance. There are regularities been captured by GP-generated classifiers in performing texture discrimination.

Experiments with explicit for-loops in genetic programming

Evolving programs with explicit loops presents major difficulties, primarily due to the massive increase in the size of the search space. Fitness evaluation becomes computationally expensive and a method for dealing with infinite loops must be implemented. We have investigated ways of dealing with these problems by the evolution of for-loops of increasing semantic complexity. We have chosen two problems - a modified Santa Fe ant problem and a sorting problem - which have natural looping constructs in their solution and a solution without loops is not possible unless the tree depth is very large. We have shown that by controlling the complexity of the loop structures it is possible to evolve smaller and more understandable programs for these problems.