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The use of Web services as a standard technology facilitates seamless business-to-business (B2B) interaction and the process of integrating various systems and applications. It enables mass knowledge distribution by providing a standard way of exposing data sources and applications/systems as Web services. These services can be created/generated, updated, and composed at runtime because they are loosely coupled. Web service composition provides the ability to compose services by manually or automatically generating a service composition plan in order to achieve the business goal, resolve a scientific issue/problem or provide new service functionality. They are reusable services that can be used to implement a business process. Web service composition can be achieved via two models: dynamic and static. This paper introduces taxonomy of Web service composition models and approaches, and provides a survey of Web service composition models and their approaches. Additionally, we present comparisons of different models and approaches for each model. [ABSTRACT FROM PUBLISHER]

In this paper a virtual ecosystem environment with basic physical law and energy concept has been proposed, this ecosystem is populated with 3D virtual creatures that are living in this environment in order to forage food. Artificial behaviours are developed to control virtual creatures. A genetic algorithm with an artificial neural network were implemented together to guarantee some of these behaviours like searching food. Foods are presented in different locations in the virtual ecosystem. The evolutionary process uses the physical properties of the virtual creatures and an external fitness function that will conduct to the expected behaviours. The experiment evolving locomoting virtual creatures show that these virtual creatures try to obtain at least one of the food sources presented in its trajectory. Our best-evolved creatures are able to reach multiple food sources during the simulation time. [ABSTRACT FROM PUBLISHER]

In this paper a new genetic algorithm based on an unsupervised fuzzy clustering is proposed for Traveling Salesman Problem (TSP). The proposed algorithm involves on three phases. In the first phase, the cities are divided into several sub-tours by the clustering algorithm. In the second phase, each partition of cities is considered as a smaller scale TSP problem and this smaller size TSP problem is solved by a genetic algorithm which gets an optimal sub-tour of the cities of this partition. In the third phase, a new technique for connecting all these sub-tours into an appropriate tour of whole cities. As well as, this appropriate tour is improved by a genetic algorithm for cluster centers and a heuristic method. The computer simulations on some standard test problems show good performance for the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Genetic Algorithms (GAs) are increasingly being explored in many areas of image analysis to solve complex optimization problems. They rapidly gained acceptance in the scientific community as powerful statistical search method whicj allows us to consider the segmentation problem as an optimization problem. In this paper, we propose the use of GAs in an integrated manner with traditional image segmentation techniques to provide an efficient segmentation and edges detection for selected natural images. The developed experimental results are compared with the results of other known existing segmentation algorithms such as K-mean clustering, and global threshold methods. The proposed method is capable of achieving a satisfactory results. Accordingly, the GAs based image segmentation method will definitely help in solving various complex image processing tasks. [ABSTRACT FROM PUBLISHER]

We propose an evolutionary algorithm for optimizing the hidden layer size of three-layer perceptrons. The optimization problem is posed in terms of finding, for each learning database, the best number of neurons to use in the hidden layer. For this, a population of three-layer perceptrons is evolved using the mean squared error as a measure of fitness. Each individual of this population is trained using the backpropagation learning algorithm. During the evolutionary process, parents are chosen using the rank selection operator and new candidate solutions are produced using the two-point crossover and mutation operators. Experiment results show that the proposed method perform well for different examples of real test data. Typical examples of these results are presented and discussed. [ABSTRACT FROM PUBLISHER]