No abstract

Graeme. 2010. Market segments based on the dominant movement patterns of tourists. Tourism Management 31 (4): pp. 464-469.

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.

Abstract-This paper presents a method which extends the use of genetic programming (GP) to a complex domain, texture segmentation. By this method, segmentation tasks are performed by texture classifiers which are evolved by GP. Small cutouts sampled from images of various textures are used for the evolution. The generated classifiers directly use pixel values as input. Based on these classifiers an algorithm which uses a voting strategy to partition texture regions is developed.The results of the investigation indicate that the proposed method is able to accurately identify the boundaries between different texture regions, even if the boundaries are not regular. The method can segment two textures as well as multiple textures. Furthermore fast segmentation can be achieved. The speed of the proposed texture segmentation method can be a hundred times faster than conventional methods.

Abstract-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 are 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 program's 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.