For service robotics, localization is an essential component required in many applications, e.g. indoor robot navigation. Today, accurate localization relies mostly on high-end devices, such as A.R.T. DTrack, VICON systems or laser scanners. These systems are often expensive and, thus, require substantial investments. In this paper, our focus is on the development of a localization method using low-priced devices, such as cameras, while being sufficiently accurate in tracking performance. Vision data contains much information and potentially yields high tracking accuracy. However, due to high computational requirements vision-based localization can only be performed at a low frequency. In order to speed up the visual localization and increase accuracy, we combine vision information with robots odometry using a Kalman-Filter. The resulting approach enables sufficiently accurate tracking performance (errors in the range of few cm) at a frequency of about 35Hz. To evaluate the proposed method, we compare our tracking performance with the high precision A.R.T. DTrack localization as ground truth. The evaluations on real robot show that our low-priced localization approach is competitive for indoor robot localization tasks.

A common practical problem in mobile robotics is the task to calibrate the robot's sensors. Although, the general mapping of the sensor data to robot-centered world coordinates is given by the hardware configuration, the parameters of this mapping vary even between robots with the same configuration. In the RoboCup domain, these parameters can change drastically after transport or physical contact during game play. It is therefore necessary to recalibrate the robots for their next assignment within a few minutes, not only in order to fulfill the future regulatory requirements of the RoboCup organization committee to keep the setup time as low as possible. As camera systems, especially omni-directional systems, are currently the most important sensors in RoboCup, a reliable and fast calibration method for the mapping of image to world coordinates is necessary. Since the RoboCup environment, i.e. the soccer field, has known dimensions and is also static, automatic calibration using the features and landmarks of the soccer field is possible if the robot is given an image from a known pose. In this paper, an efficient evolutionary approach to automatic camera calibration is presented, which is independent of the hardware configuration. It only requires a quality function for the parameter settings, which allows lazy evaluation. To meet the time constraints given for this real-world optimization problem, a novel mutation operator is introduced to enhance the performance of the evolutionary algorithm. It samples a number of alternative solutions using a high rate of lazy evaluation, before deciding on the true mutative change applied on the given individual. This new mutation operator proves to be fast and most reliable on the camera to world calibration problem.

The portfolio selection problem is a prominent example for multi-objective optimization in financial engineering. While for some problem instances of the portfolio selection problem there are efficient optimization algorithms, other problem instances can only be addressed by means of meta-heuristics like evolutionary algorithms. These more complicated problem instances include portfolio selection with multiple quadratic objectives or with non-linear constraints, like cardinality constraints. Evolutionary algorithms allow hybridization with local search heuristics, resulting in so called memetic algorithms. Such memetic approaches to the portfolio selection problem seem to be an interesting alternative. Unfortunately, the interaction between the evolutionary global search and the local search heuristic is complicated and difficult to understand. In this paper we evaluate the hybridization of a multi-objective evolutionary algorithm and a quadratic programming local search on multiple instances of the constrained and unconstrained portfolio selection problem using a problem specific representation. This multi-objective memetic algorithm proves to be a two-edged approach: On the one hand it improves the convergence rate for some problem instances. While on other hand problem instances the local search causes a neutral search space and eventually premature convergence. This paper investigates this behavior more closely, offers a plausible explanation and also outlines a possible remedy.

In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different mathematical models on the inference problem. They are used to model the underlying dynamic system of artificial regulatory networks. The dynamics of the artificial systems represent different basic types of behavior,dimensionality and mathematical properties. They are all created with three commonly used approaches, namely linear weight matrices, H-systems, and S-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. However, in many publications only one algorithm is used without any comparison to other optimization methods. Thus, we introduce a framework to systematically apply evolutionary algorithms for further comparative analysis.

In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different evolutionary algorithms on the inference problem. These algorithms are used to evolve an underlying quantitative mathematical model. The dynamics of the regulatory system are modeled with two commonly used approaches, namely linear weight matrices and S-systems and a novel formulation, namely H-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. However, in many publications only one algorithm is used without any comparison to other optimization methods. Thus, we introduce a framework to systematically apply evolutionary algorithms and different types of mutation and crossover operators to the inference problem for further comparative analysis.

Many approaches for extracting landmarks and objects from a camera image based on their color coding were published in the RoboCup domain. They are quite sophisticated and tuned to the typical RoboCup scenario of constant bright lighting using a static subdivision of the color space into different color classes. However, such algorithms would soon be of limited use, as the future requirements of RoboCup include the possibility to play under changing and finally natural lighting. This paper presents an algorithm for automatic online color training, which is able to robustly adapt the mapping of colors to color classes onto different lighting situations online. Using the ACT algorithm a robot will be able to play a RoboCup match while the illumination of the field varies

MOTIVATION: JCell is a Java-based application for reconstructing gene regulatory networks from experimental data. The framework provides several algorithms to identify genetic and metabolic dependencies based on experimental data conjoint with mathematical models to describe and simulate regulatory systems. Owing to the modular structure, researchers can easily implement new methods. JCell is a pure Java application with additional scripting capabilities and thus widely usable, e.g. on parallel or cluster computers. AVAILABILITY: The software is freely available for download at http://www-ra.informatik.uni-tuebingen.de/software/JCell.

In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying parameters of mathematical models can be found in literature. The problem of reconstructing regulatory systems from experimental data is often multimodal and thus appropriate optimization strategies become necessary. Thus, we propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step

In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. The problem often is multi-modal and therefore appropriate optimization strategies become necessary. We propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks than standard algorithms. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step.

This work addresses the capability to use concentration gridmaps to locate a static gas source. In previous works it was found that depending on the shape of the mapped gas distribution (corresponding to different airflow conditions) the gas source location can be sometimes approximated with high accuracy by the maximum in the concentration map while this is not possible in other cases. This paper introduces a method to distinguish both cases by analysing the shape of the obtained concentration map in terms of a model of the time-averaged gas distribution known from physics. The parameters of the model that approximates the concentration map most closely are determined by nonlinear least squares fitting using evolution strategies (ES). The best fit also provides a better estimate of the gas source position in situations where the concentration maximum estimate fails. Different methods to select the most truthful estimate are introduced in this work and a comparison regarding their accuracy is presented, based on a total of 34h of concentration mapping experiments.

In this paper we address the problem of finding gene regulatory networks from artificial data sets of DNA microarray experiments. Some researchers suggested evolutionary algorithms for this purpose. We suggest to use an enhancement for evolutionary algorithms to infer the parameters of the nonlinear system given by the observed data more reliably and precisely. At present, we use S-Systems because they are a general mathematical model for simulating the complex interactions of gene regulatory networks. Due to the limited number of available data, the inferring problem is highly under-determined and ambiguous. Further on, the problem often is highly multi-modal and therefore appropriate optimization strategies become necessary. We propose to use an island model to maintain diversity in the EA population to prevent premature convergence and to raise the probability of finding the global optimum.

In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. As underlying mathematical model we used S-Systems, a quantitative model, which recently has found increased attention in the literature. Due to the complexity of the inference problem some researchers suggested evolutionary algorithms for this purpose. We introduce enhancements to this optimization process to infer the parameters of sparsely connected non-linear systems given by the observed data more reliably and precisely. Due to the limited number of available data the inferring problem is under-determined and ambiguous. Further on, the problem often is multi-modal and therefore appropriate optimization strategies become necessary. In this paper, we propose a new method, which evolves the topology as well as the parameters of the mathematical model to find the correct network. This method is compared to standard algorithms found in the literature.

In this paper we investigate the impact of different crossover operators for a real-valued evolutionary algorithm on the constrained portfolio selection problem based on the Markowitz mean-variance model. We also introduce an extension of a real-valued genotype, which increases the performance of the evolutionary algorithm significantly, independent of the crossover operator used. This extension is based on the effect that most efficient portfolios only consist of a selection of few assets. Therefore, the portfolio selection problem is actually a combination of a knapsack and continuous parameter problem. We also introduce a repair mechanism and examine the impact of Lamarckism on the performance of the evolutionary algorithm.

Evolutionary algorithms (EA) are excellent optimization tools for complex high-dimensional multimodal problems. However, they require a very large number of problem function evaluations. In many engineering and design optimization problems a single fitness evaluation is very expensive or time consuming. Therefore, standard evolutionary computation methods are not practical for such applications. Applying models as a surrogate of the true fitness function is a quite popular approach to handle this restriction. It is straightforward that the success of this approach depends highly on the quality of the approximation model. We propose a controlled model assisted evolution strategy (C-MAES), which uses a support vector regression (SVR) approximation by preselecting the most promising individuals. The model assistance on the evolutionary optimization process is dynamically controlled by a model quality based on the number of correctly preselected individuals. Numerical results from extensive simulations on high dimensional test functions including noisy functions and noisy functions with changing noise level are presented. The proposed C-MAES algorithm with controlled model assistance has a much better convergence rate and achieves better results than the model assisted algorithms without model control.

In many engineering optimization problems, the number of fitness function evaluations is limited by time and cost. These problems pose a special challenge to the field of evolutionary computation, since existing evolutionary methods require a very large number of problem function evaluations. One popular way to address this challenge is the application of approximation models as a surrogate of the real fitness function. We propose a model assisted evolution strategy, which uses a Gaussian process approximation model to preselect the most promising solutions. To refine the preselection process we determine the likelihood of each individual to improve the overall best found solution. Due to this, the new algorithm has a much better convergence behavior and achieves better results than standard evolutionary optimization approaches with less fitness evaluations. Numerical results from extensive simulations on several high dimensional test functions including multimodal functions are presented.