THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

/pdf/evolving-artificial-neural-networks-4kzrbu7pkn.pdf

Evolving artificial neural networks

List of Figures. List of Tables. Preface. Contributing Authors. Series Foreword. Part I: Foundations. 1. An Introduction to Evolutionary Algorithms J.A. Lozano. 2. An Introduction to Probabilistic Graphical Models P. Larranaga. 3. A Review on Estimation of Distribution Algorithms P. Larranaga. 4. Benefits of Data Clustering in Multimodal Function Optimization via EDAs J.M. Pena, et al. 5. Parallel Estimation of Distribution Algorithms J.A. Lozano, et al. 6. Mathematical Modeling of Discrete Estimation of Distribution Algorithms C. Gonzalez, et al. Part II: Optimization. 7. An Empiricial Comparison of Discrete Estimation of Distribution Algorithms R. Blanco., J.A. Lozano. 8. Results in Function Optimization with EDAs in Continuous Domain E. Bengoetxea, et al. 9. Solving the 0-1 Knapsack Problem with EDAs R. Sagarna, P. Larranaga. 10. Solving the Traveling Salesman Problem with EDAs V. Robles, et al. 11. EDAs Applied to the Job Shop Scheduling Problem J.A. Lozano, A. Mendiburu. 12. Solving Graph Matching with EDAs Using a Permutation-Based Representation E. Bengoetxea, et al. Part III: Machine Learning. 13. Feature Subset Selection by Estimation of Distribution Algorithms I. Inza, et al. 14. Feature Weighting for Nearest Neighbor by EDAs I. Inza, et al. 15. Rule Induction by Estimation of Distribution Algorithms B. Sierra, et al. 16. Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs L.M. de Campos, et al.17. Comparing K-Means, GAs and EDAs in Partitional Clustering J. Roure, et al. 18. Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms C. Cotta, et al. Index.

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Software visualization is an area of computer science devoted to supporting the understanding and effective use of algorithms. The application of software visualization to Evolutionary Computation has been receiving increasing attention during the last few years. In this paper we apply visualization technique to an evolutionary algorithm for multilayer perceptron training. Our goal is to better understand its internal behavior in order to improve the evolutionary part of the method. As a result of applying this this technique several deficiencies in the method have been discovered.

Evolutionary Computation Visualization: Application to G-PROP

In Gigabit class networks, the physical transmission time is small compared to the time required to process the TCP/IP protocol stack. Thus, the usefulness of lightweight protocols that reduce the communication software overhead is even higher, as the performance demands shift to the network interface hardware and/or software. The communication protocol CLIC has recently been proposed for efficient communication in clusters using the Linux operating system. It uses an approach to optimize the communication performance in a cluster of computers different from that of providing a user-level interface that removes the operating system (OS) mediation in the communication path. Thus, besides reducing the latencies and increasing the bandwidth figures even for short messages, CLIC meets other requirements such as multiprogramming, portability, protection against corrupted programs, reliable message delivery, direct access to the network for all applications, etc., by providing an optimized OS support to reliable and efficient network software, that avoids the TCP/IP protocol stack. In this paper we show how new facilities and improved performance of network interface cards (NICs) have been incorporated to CLIC in order to take advantage of the gigabit network technology.

The lightweight protocol CLIC on Gigabit Ethernet

Nowadays, the growth of the computer networks and the expansion of the Internet have made the security to be a critical issue. In fact, many proposals for Intrusion Detection/Prevention Systems (IDS/IPS) have been proposed. These proposals try to avoid that corrupt or anomalous traffic reaches the user application or the operating system. Nevertheless, most of the IDS/IPS proposals only distinguish between normal traffic and anomalous traffic that can be suspected to be a potential attack. In this paper, we present a IDS/IPS approach based on Growing Hierarchical Self-Organizing Maps (GHSOM) which can not only differentiate between normal and anomalous traffic but also identify different known attacks. The proposed system has been trained and tested using the well-known DARPA/NSL-KDD datasets and the results obtained are promising since we can detect over 99,4% of the normal traffic and over 99,2 % of attacker traffic. Moreover, the system can be trained on-line by using the probability labeling method presented on this paper.

Network intrusion prevention by using hierarchical self-organizing maps and probability-based labeling

A new method is proposed for the blind separation of mixed digital or analog sources, based on geometrical considerations concerning the observation space. For p mixed sources, where p is greater than or equal to two, the new approach considers the p-dimensional hyperparallelepiped formed in the observation space, and by means of a neural network with w/sub if/ weights, computes the coordinates of p vectors corresponding to the image of orthogonal inputs in the source space. These coordinates provide the columns of the unknown mixture matrix A, with a/sub if/ elements, and the neural network recursively separates the unknown sources, S/sub 0/. This geometrical procedure does not need the computation of any order of statistics, using instead primitives that may easily be implemented by hardware and it has a polynomial complexity (p/sup 3/) which depends on the number of sources (p).

http://ieeexplore.ieee.org/iel3/3972/11461/00542758.pdf

New geometrical approach for blind separation of sources mapped to a neural network

Techniques are provided for generating a network complexity score. In one approach, a plurality of node configurations that includes a first node configuration and a second node configuration are analyzed. A network complexity score is generated for a network based, at least in part, on one or more of (a) a degree of similarity between the first node configuration and the second node configuration or (b) a dependency between the first node configuration and the second node configuration.

Alberto Prieto

Papers

Evolutionary Computation Visualization: Application to G-PROP

The lightweight protocol CLIC on Gigabit Ethernet

Network intrusion prevention by using hierarchical self-organizing maps and probability-based labeling

New geometrical approach for blind separation of sources mapped to a neural network

Analyzing network configuration complexity