Natural immune systems protect animals from dangerous foreign pathogens, including bacteria, viruses, parasites, and toxins. Their role in the body is analogous to that of computer security systems in computing. Although there are many differences between living organisms and computer systems, this article argues that the similarities are compelling and could point the way to improved computer security. Improvements can be achieved by designing computer immune systems that have some of the important properties illustrated by natural immune systems. These include multi-layered protection, highly distributed detection and memory systems, diversity of detection ability across individuals, inexact matching strategies, and sensitivity to most new foreign patterns. We first give an overview of how the immune system relates to computer security. We then illustrate these ideas with two examples.

The transjugular intrahepatic portosystemic shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff's experience, the consequences of the TIPS are not homogeneous for all the patients and a subgroup of them dies in the first six months after the TIPS placement. Actually, there is no risk indicator to identify this group, before treatment. An investigation for predicting the survival of cirrhotic patients treated with TIPS was carried out using a clinical database with 107 cases and 77 attributes. Naive-Bayes rule and ID3 decision tree classifier were used with the whole set of attributes in the prediction of the survival of cirrhotic patients for the first six months after the placement of the TIPS. Due to the large amount of attributes and with the aim of obtaining more accurate and understandable models, FSS-PBIL (Feature Subset Selection by Population-Based Incremental Learning), a new randomized, population-base...

In this paper we analyze the application of parallel and sequential evolutionary algorithms to the automatic test data generation problem. The problem consists of automatically creating a set of input data to test a program. This is a fundamental step in software development and a time consuming task in existing software companies. Canonical sequential evolutionary algorithms have been used in the past for this task. We explore here the use of parallel evolutionary algorithms. Evidence of greater efficiency, larger diversity maintenance, additional availability of memory/CPU, and multi-solution capabilities of the parallel approach, reinforce the importance of the advances in research with these algorithms. We describe in this work how canonical genetic algorithms (GAs) and evolutionary strategies (ESs) can help in software testing, and what the advantages are (if any) of using decentralized populations in these techniques. In addition, we study the influence of some parameters of the proposed test data generator in the results. For the experiments we use a large benchmark composed of twelve programs that includes fundamental algorithms in computer science.

Abstract Predicting the three-dimensional structure of proteins from their linear sequence is one of the major challenges in modern biology. It is widely recognized that one of the major obstacles in addressing this question is that the “standard ” computational approaches are not powerful enough to search for the correct structure in the huge conformational space. Genetic algorithms, a cooperative computational method, have been successful in many difficult computational tasks. Thus, it is not surprising that in recent years several studies were performed to explore the possibility of using genetic algorithms to address the protein structure prediction problem. In this review, a general framework of how genetic algorithms can be used for structure prediction is described. Using this framework, the significant studies that were published in recent years are discussed and compared. Applications of genetic algorithms to the related question of protein alignments are also mentioned. The rationale of why genetic algorithms are suitable for protein structure prediction is presented, and future improvements that

Gentropy is a genetic programming system that evolves two-dimensional procedural textures. It synthesizes textures by combiningmathematical and image manipulation functions into formulas. A formula can be reevaluated with arbitrary texture-space coordinates, to generate a new portion of the texture in texture space. Most evolutionary art programs are interactive, and require the user to repeatedly choose the best images from a displayed generation. Gentropy uses an unsupervised approach, where one or more target texture image is supplied to the system, and represent the desired texture features, such as colour, shape and smoothness (contrast). Then, Gentropy evolves textures independent of any further user involvement. The evolved texture will not be identical to the target texture, but rather, will exhibit characteristics similar to it. When more than one texture is supplied as a target, multi-objective feature analysis is performed. These feature tests may be combined and given di#erent priorities during evaluation. It is therefore possible to use several target images, each with its own fitness function measuring particular visual characteristics. Gentropy also permits the use of multiple subpopulations, each of which may use its own texture evaluation criteria and target texture.

Automatic mineral identification using evolutionary computation technology is discussed. Thin sections of mineral samples are photographed digitally using a computer -controlled rotating polarizer stage on a petrographic microscope. A suite of image processing functions is applied to the images. Filtered image data for identified mineral grains is then selected for use as training data for a genetic programming system, which automatically synthesizes computer programs that identify these grains. The evolved programs use a decision-tree structure that compares the mineral image values with one other, resulting in a thresholding analysis of the multi-dimensional colour and textural space of the mineral Key words: Mineral classification -- Genetic programming -- Feature space thresholding 1

Much of the difficulty in architectural design is in integrating and making explicit the knowledge of the many converging disciplines (engineering, sociology, ergonomics and psychology, to name a few), the building requirements from many viewpoints, and to model the complex system interactions. The many rôles of the architect simply compound this. This paper describes a system currently under development—a 3D design medium and intelligent analysis tool, to help elicit and make explicit these requirements. The building model is used to encapsulate information throughout the building lifecycle, from inception and master planning to construction and ‘lived-in’ use. From the tight relationship between material behaviour of the model, functional analysis and visual feedback, the aim is to help in the resolution of functional needs, so that the building meets not only the aims of the architect, but the needs of the inhabitants, users and environment. The Problem of Designing the Built Environment It is often said that architecture is the mother of the arts since it embodies all the

Genoud is an R function that combines evolutionary algorithm methods with a derivativebased (quasi-Newton) method to solve difficult optimization problems. Genoud may also be used for optimization problems for which derivatives do not exist. Genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood) is nonlinear in the model’s parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms) are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.

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