Almost all analyses of time complexity of evolutionary algorithms (EAs) have been conducted for (1+1) EAs only. Theoretical results on the average computation time of population-based EAs are few. However, the vast majority of applications of EAs use a population size that is greater than one. The use of population has been regarded as one of the key features of EAs. It is important to understand in depth what the real utility of population is in terms of the time complexity of EAs, when EAs are applied to combinatorial optimization problems. This paper compares (1 + 1) EAs and (N + N) EAs theoretically by deriving their first hitting time on the same problems. It is shown that a population can have a drastic impact on an EA’s average computation time, changing an exponential time to a polynomial time (in the input size) in some cases. It is also shown that the first hitting probability can be improved by introducing a population. However, the results presented in this paper do not imply that population-based EAs will always be better than (1 + 1) EAs for all possible problems. I.

This research examines the pattern of Web information seeking in four groups of nurses with different combinations of domain expertise and Web expertise. Protocols were gathered as the nurses carried out information-seeking tasks in the domain of osteoporosis. Domain and Web novices searched breadth-first and did little or no evaluation of the results. Domain expert/Web novices also searched breadth-first but evaluated information more thoroughly using osteoporosis knowledge. Domain novice/Web experts searched in a mixed, breadth-first/depth-first pattern and attempted to evaluate information using general criteria. Domain expert/Web experts carried out depth-first searches, following deep trails of information and evaluated information based on the most varied and sophisticated criteria. The results suggest that there are distinct differences in searching patterns related to expertise. Implications of these findings and suggestions for future research are provided.

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Decision making often requires the analysis of large amount of data and complex relations. Computerized tools designed and implemented for such purposes are called Decision Support Systems (DSS). A DSS, which is typically a problem specific tool, usually helps in the evaluation of consequences of given decisions and may advise what decision would be the best for achieving a given set of goals. In such cases, an analysis of a mathematical model can support rational decision making. The paper provides an overview of the methodology of the design and deals with practical aspects related to implementations of model-based decision support systems. In particular, different approaches to the analysis of a model using simulation and optimization are summarized. Various optimization techniques are discussed in this context, including multi-criteria optimization used for a model analysis. The paper summarizes also problems of hardware selection and of software development. Modular software tools...

Estimation of distribution algorithms (EDAs) try to solve an optimization problem by finding a probability distribution focussed around its optima. For this purpose they conduct a sampling-evaluation-adjustment cycle, where search points are sampled with respect to a probability distribution, which is adjusted according to the evaluation of the sampled points. Although there are many successful experiments suggesting the usefulness of EDAs, there are only few rigorous theoretical results apart from convergence results without time bounds. Here we present first rigorous runtime analyses of a simple EDA, the compact genetic algorithm, for linear pseudo-boolean functions on n variables. We prove a number of results showing that not all linear functions have the same asymptotical runtime.

Advanced information retrieval systems providing automated assistance offer the opportunity to greatly enhance the effectiveness of the information retrieval process. One issue in designing such systems is determining the effect that the automated assistance has on the tasks and sequence of tasks within this process. Using verbal protocol data and transaction log analysis, we present a taxonomy of tasks when utilizing information retrieval systems with automated assistance, along with a temporal analysis of when interaction with the automated assistance occurs. Results indicate that there is a predictable pattern of user interaction with automated assistance with implications for the design of information retrieval systems.

Optimal solutions of interior point algorithms for linear and quadratic programming and linear complementarity problems provide maximal complementary solutions. Maximal complementary solutions can be characterized by optimal (tri)partitions. On the other hand, the solutions provided by simplex--based pivot algorithms are given in terms of complementary bases. A basis identification algorithm is an algorithm which generates a complementary basis, starting from any complementary solution. A tripartition identification algorithm is an algorithm which generates a maximal complementary solution (and its corresponding tripartition), starting from any complementary solution. In linear programming such algorithms were respectively proposed by Megiddo in 1991 and Balinski and Tucker in 1969. In this paper we will present identification algorithms for quadratic programming and linear complementarity problems with sufficient matrices. The presented algorithms are based on the principal...

In practical applications evaluating a fitness function is frequently subject to noise, i. e., the “true fitness” is disturbed by some random variations. Evolutionary algorithms (EAs) are often successfully applied to noisy problems, where they have turned out to be particularly robust. Theoretical results on the behavior of EAs for noisy functions are comparatively very rare, especially for discrete search spaces. Here we present an analysis of the (1+1) EA for a noisy variant of OneMax and compute the maximal noise strength allowing the (1+1) EA a polynomial runtime asymptotically exactly. The methods used in the proofs are presented in a general form with clearly stated conditions in order to simplify further applications.

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Conducting user studies and user evaluations of information retrieval systems requires the collection of specialized data, namely user interactions with the system. These interactions occur both on the server and the client side. Collecting the client-side data in a format for easy analysis has proven to be a difficult task for many researchers. In this paper, we report on a software application that logs user interactions with information searching systems. Developed in Visual Basic, the application is simple to install, ease to modify, and logs session actions to an ASCII text file for easy upload into a variety of analysis packages. The application records many of the typical user interactions with searching systems, including submitted queries, Web site navigation, scrolling, bookmarking, among others. The application time stamps each interaction. Interested researchers may download the application via a provided uniform resource locator.