ShapeFuture will drive innovation in fundamental Electronic Components and Systems (ECS) that are essential for robust, powerful, fail-operational and integrated perception, cognition, AI-enabled decision making, resilient automation and computing, as well as communications, for highly automated vehicles. The overarching vision of ShapeFuture is to bring ECS Innovation to the heart of Europe’s Mobility Transformation, thereby elevating sovereignty by perfecting programmable ECS solutions for intelligent, safe, connected, and highly automated vehicles. In this paper, we detail not only the vision and mission of the ShapeFuture project, but we also showcase the results achieved during the first year.

Machine learning models play a key role in safety-critical applications, such as autonomous vehicles and advanced driver assistance systems, where their robustness during inference is essential to ensure reliable operation. Sensor faults, however, can corrupt input signals, potentially leading to severe model failures that compromise reliability. In this context, pretraining emerges as a powerful approach for learning expressive representations applicable to various downstream tasks. Among existing techniques, masking represents a promising direction for learning representations that are robust to corrupted input data. In this work, we extend this concept by specifically targeting robustness to sensor outages during pretraining. We propose a self-supervised masking scheme that simulates common sensor failures and explicitly trains the model to recover the original signal. We demonstrate that the resulting representations significantly improve the robustness of predictions to seen and unseen sensor failures on a vehicle dynamics dataset, maintaining strong downstream performance under both nominal and various fault conditions. As a practical application, we deploy the method on a modified Lexus LC 500 and show that the pretrained model successfully operates as a substitute for a physical sensor in a closed-loop control system. In this autonomous racing application, a supervised baseline trained without sensor failures may cause the vehicle to leave the track. In contrast, a model trained using the proposed masking scheme enables reliable racing performance in the presence of sensor failures.

Evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots’ morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots’ controller parameters.

Abstract Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimization. Available taxonomies lack the embedding of current approaches in the larger context of this broad field. This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies. A particular focus lies on algorithms using surrogates, nature-inspired designs, and those created by automatic algorithm generation. The extracted features of algorithms, their main concepts, and search operators, allow us to create a set of classification indicators to distinguish between a small number of classes. The features allow a deeper understanding of components of the search strategies and further indicate the close connections between the different algorithm designs. We present intuitive analogies to explain the basic principles of the search algorithms, particularly useful for novices in this research field. Furthermore, this taxonomy allows recommendations for the applicability of the corresponding algorithms.

This article introduces a benchmarking framework that allows rigorous evaluation of parallel model-based optimizers for expensive functions. The framework establishes a relationship between estimated costs of parallel function evaluations (on real-world problems) to known sets of test functions. Such real-world problems are not always readily available (e.g., confidentiality and proprietary software). Therefore, new test problems are created by Gaussian process simulation. The proposed framework is applied in an extensive benchmark study to compare multiple state-of-the-art parallel optimizers with a novel model-based algorithm, which combines ideas of an explorative search for global model quality with parallel local searches to increase function exploitation. The benchmarking framework is used to configure good batch size setups for parallel algorithms systematically based on landscape properties. Furthermore, we introduce a proof of concept for a novel automatic batch size configuration. The predictive quality of the batch size configuration is evaluated on a large set of test functions and the functions generated by Gaussian process simulation. The introduced algorithm outperforms multiple state-of-the-art optimizers, especially on multimodal problems. Additionally, it proves to be particularly robust over various problem landscapes, and performs well with all tested batch sizes. Consequently, this makes it well suited for black-box kinds of problems.

Hospitals and health-care institutions need to plan the resources required for handling the increased load, i.e., beds and ventilators during the COVID-19 pandemic. BaB-Sim.Hospital, an open-source tool for capacity planning based on discrete event simulation, was developed over the last year to support doctors, administrations, health authorities, and crisis teams in Germany. To obtain reliable results, 29 simulation parameters such as durations and probabilities must be specified. While reasonable default values were obtained in detailed discussions with medical professionals, the parameters have to be regularly and automatically optimized based on current data.We investigate how a set of parameters that is tailored to the German health system can be transferred to other regions. Therefore, we use data from the UK. Our study demonstrates the flexibility of the discrete event simulation approach. However, transferring the optimal German parameter settings to the UK situation does not work-parameter ranges must be modified. The adaptation has been shown to reduce simulation error by nearly 70%. The simulation-via-optimization approach is not restricted to health-care institutions, it is applicable to many other real-world problems, e.g., the development of new elevator systems to cover the last mile or simulation of student flow in academic study periods.

This paper presents a new methodology for complex system design by means of optimisation techniques. Within the Model-based Engineering approach, optimisation algorithms are used to explore optimal solutions of highly coupled and nonlinear systems. In such scenario, the optimal technology has to be identified and its settings have to be optimised. Relying on optimisation strategies for both the challenges brings to complex mixed-variable problem formulations involving continuous, integer and categorical parameters. Furthermore, part of the parameters are required only if certain technologies are adopted, bringing to variable-size formulations that standard optimisers cannot manage. Therefore, the proposed methodology relies on the use of variable-size mixed-variable global optimiser Structured-Chromosome Genetic Algorithm (SCGA). The advantages of this new method are shown by applying it for solving a space system preliminary design. In particular, two variants have been implemented distinguished by two different levels of complexity. To better appreciate the proposed approach, the same problems have been reformulated to be treated by a well known and appreciated optimiser in the field of spacecraft design, Multi- Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA). The final results of the two approaches are compared and commented.

This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configuration as a decision variable of an automatic optimisation tool. This task requires the coupling of an estimation routine and an optimisation algorithm. For the former, SU2 flow solver has been used. The Structured-Chromosome Genetic Algorithm (SCGA) optimiser has been employed to search for the optimal HLD. SCGA can overcome the limitations dictated by standard fixed-size continuous optimisation algorithms. Indeed, using hierarchical formulations, it can manage configurational decisions that are conventionally the responsibility of expert designers. The search algorithm bases its strategy on revised genetic operators conceived for handling hierarchical search spaces. The presented research not only shows the practicability of delegating to a specialised optimisation algorithm the complete HLD design but is intended to be a proof of concept for the whole field of multidisciplinary design optimisation. Indeed, the aerospace sector as a whole would benefit by reducing human intervention from the decision process.

Real-world problems such as computational fluid dynamics simulations and finite element analyses are computationally expensive. A standard approach to mitigating the high computational expense is Surrogate-Based Optimization (SBO). Yet, due to the high-dimensionality of many simulation problems, SBO is not directly applicable or not efficient. Reducing the dimensionality of the search space is one method to overcome this limitation. In addition to the applicability of SBO, dimensionality reduction enables easier data handling and improved data and model interpretability. Regularization is considered as one state-of-the-art technique for dimensionality reduction. We propose a hybridization approach called Regularized-Surrogate-Optimization (RSO) aimed at overcoming difficulties related to high-dimensionality. It couples standard Kriging-based SBO with regularization techniques. The employed regularization methods are based on three adaptations of the least absolute shrinkage and selection operator (LASSO). In addition, tree-based methods are analyzed as an alternative variable selection method. An extensive study is performed on a set of artificial test functions and two real-world applications: the electrostatic precipitator problem and a multilayered composite design problem. Experiments reveal that RSO requires significantly less time than standard SBO to obtain comparable results. The pros and cons of the RSO approach are discussed, and recommendations for practitioners are presented.

Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to decide which point to evaluate next. When Kriging is used as the surrogate model of choice (also called Bayesian optimization), one of the most frequently chosen criteria is expected improvement. We argue that the popularity of expected improvement largely relies on its theoretical properties rather than empirically validated performance. Few results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of the surrogate model. We benchmark both infill criteria in an extensive empirical study on the 'BBOB' function set. This investigation includes a detailed study of the impact of problem dimensionality on algorithm performance. The results support the hypothesis that exploration loses importance with increasing problem dimensionality. A statistical analysis reveals that the purely exploitative search with the predicted value criterion performs better on most problems of five or higher dimensions. Possible reasons for these results are discussed. In addition, we give an in-depth guide for choosing the infill criteria based on prior knowledge about the problem at hand, its dimensionality, and the available budget.

This paper addresses the problem of autonomous scheduling of space objects' observations from a network of tracking stations to enhance the knowledge of their orbit while respecting allocated resources. This task requires the coupling of a state estimation routine and an optimisation algorithm. As for the former, a sequential filtering approach to estimate the satellite state distribution conditional on received indirect measurements has been employed. To generate candidates, i.e. observation campaigns, a Structured-Chromosome Genetic Algorithm optimiser has been developed, which is able to address the issue of handling mixed-discrete global optimisation problems with variable-size design space. The search algorithm bases its strategy on revised genetic operators that have been reformulated for handling hierarchical search spaces. The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.

Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0.

In the last years, reinforcement learning received a lot of attention. One method to solve reinforcement learning tasks is Neuroevolution, where neural networks are optimized by evolutionary algorithms. A disadvantage of Neuroevolution is that it can require numerous function evaluations, while not fully utilizing the available information from each fitness evaluation. This is especially problematic when fitness evaluations become expensive. To reduce the cost of fitness evaluations, surrogate models can be employed to partially replace the fitness function. The difficulty of surrogate modeling for Neuroevolution is the complex search space and how to compare different networks. To that end, recent studies showed that a kernel based approach, particular with phenotypic distance measures, works well. These kernels compare different networks via their behavior (phenotype) rather than their topology or encoding (genotype). In this work, we discuss the use of surrogate model-based Neuroevolution (SMB-NE) using a phenotypic distance for reinforcement learning. In detail, we investigate a) the potential of SMB-NE with respect to evaluation efficiency and b) how to select adequate input sets for the phenotypic distance measure in a reinforcement learning problem. The results indicate that we are able to considerably increase the evaluation efficiency using dynamic input sets.

Surrogate Model Based Optimization (SMBO) is an established technique for handling computationally expensive optimization problems. One important application is the optimization of Particle Reinforced Metal Matrix Composites (PRMMCs). Multi-phase materials are gaining attention. Their performance is strongly affected by microscale properties. By optimizing the microscale structure, these materials can be tailored to satisfy specific requirements. Current manufacturing techniques have limited control over the distribution of reinforcing particles and are subject to considerable uncertainty. Moreover, the simulation and optimization of PRMMCs requires significant computational effort. We propose an approach that tackles the problem of optimizing the characteristics of PRMMCs subject to uniaxial load, by improving the particles' spatial distribution. The optimization problem is split into a bilevel problem: The upper-level optimization aims to find the particle distribution parameters which maximize the PRMMC limit load. Due to potentially infeasible distributions, the lower-level problem attempts to create a particle placement that reflects the specifications of an upper-level candidate solution.

The availability of several CPU cores on current computers enables parallelization and increases the computational power significantly. Optimization algorithms have to be adapted to exploit these highly parallelized systems and evaluate multiple candidate solutions in each iteration. This issue is especially challenging for expensive optimization problems, where surrogate models are employed to reduce the load of objective function evaluations.

When designing or developing optimization algorithms, test functions are crucial to evaluate performance. Often, test functions are not sufficiently difficult, diverse, flexible or relevant to real-world applications. Previously, test functions with real-world relevance were generated by training a machine learning model based on real-world data. The model estimation is used as a test function. We propose a more principled approach using simulation instead of estimation. Thus, relevant and varied test functions are created which represent the behavior of real-world fitness landscapes. Importantly, estimation can lead to excessively smooth test functions while simulation may avoid this pitfall. Moreover, the simulation can be conditioned by the data, so that the simulation reproduces the training data but features diverse behavior in unobserved regions of the search space. The proposed test function generator is illustrated with an intuitive, one-dimensional example. To demonstrate the utility of this approach it is applied to a protein sequence optimization problem. This application demonstrates the advantages as well as practical limits of simulation-based test functions.

Cyclone separators are filtration devices frequently used in industry, e.g., to filter particles from flue gas. Optimizing the cyclone geometry is a demanding task. Accurate simulations of cyclone separators are based on time consuming computational fluid dynamics simulations. Thus, the need for exploiting cheap information from analytical, approximative models is evident. Here, we employ two multi-objective optimization algorithms on such cheap, approximative models to analyze their optimization performance on this problem. Under various limitations, we tune both algorithms with Sequential Parameter Optimization (SPO) to achieve best possible results in shortest time. The resulting optimal settings are validated with different seeds, as well as with a different approximative model for collection efficiency. Their optimal performance is compared against a model based approach, where multi-objective SPO is directly employed to optimize the Cyclone model, rather than tuning the optimization algorithms. It is shown that SPO finds improved parameter settings of the concerned algorithms and performs excellently when directly used as an optimizer.

Real-world optimization problems may require time consuming and expensive measurements or simulations. Recently, the application of surrogate model-based approaches was extended from continuous to combinatorial spaces. This extension is based on the utilization of suitable distance measures such as Hamming or Swap Distance. In this work, such an extension is implemented for Kriging (Gaussian Process) models. Kriging provides a measure of uncertainty when determining predictions. This can be harnessed to calculate the Expected Improvement (EI) of a candidate solution. In continuous optimization, EI is used in the Efficient Global Optimization (EGO) approach to balance exploitation and exploration for expensive optimization problems. Employing the extended Kriging model, we show for the first time that EGO can successfully be applied to combinatorial optimization problems. We describe necessary adaptations and arising issues as well as experimental results on several test problems. All surrogate models are optimized with a Genetic Algorithm (GA). To yield a comprehensive comparison, EGO and Kriging are compared to an earlier suggested Radial Basis Function Network, a linear modeling approach, as well as model-free optimization with random search and GA. EGO clearly outperforms the competing approaches on most of the tested problem instances.

Heuristic search algorithms have been successfully applied to solve many problems in practice. Their design, however, has increased in complexity as the number of parameters and choices for operators and algorithmic components is also expanding. There is clearly the need for providing the final user with automated tools to assist the tuning, design and assessment of heuristic optimisation methods. In recent years a growing number workshops and tracks has been held to address these issues. In 2010, the Parallel Problem Solving from Nature (PPSN) conference hosted two workshops, which decided to joint efforts to organise this journal special issue. The workshop “Self-Tuning, Self-Configuring and Self-Generating Search Heuristics,” distinguished three general processes in automated heuristic design: 1) tuning: the process of adjusting the algorithm's control parameters, 2) configuring: the process of selecting and using existing algorithmic components such as search operators, construction heuristics or acceptance criteria, and 3) generating: the process of creating altogether new heuristics (or heuristic components) from the basic sub-components of previously existing methods. Machine learning, meta-modelling and multilevel search approaches can and have been applied to automate these three processes. The workshop introduced the term ‘Self-* Search’, which is now the name of a track in GECCO, which started in 2011 and is also being held this year. The other workshop “Methods for the Assessment of Computational Systems” stressed the idea that the experimental analysis of computational systems inspired by nature can be made more sound and effective by the use of appropriate experimental methods. More severe requirements have been transmitted to draw objective conclusions from computational experiments, while at the same time the design and configuration of the computational systems can be improved by profitable ways of looking into the data collected.The quest for methods to automate the design and assessment of heuristic search methods is spawning a considerable amount of interdisciplinary research, mainly between the fields of computer science, artificial intelligence, optimization, statistics and machine learning. This special issue gathers contributions at the interface of these topics. It comprises five high quality papers that were selected after a rigorous reviewing process.The first two articles are related to the automatic, online configuration of heuristic search methods. Adaptive memetic algorithms (Ong et al., 2006) and selective hyper-heuristics (Burke et al., 2010) have developed separately. However, they share key research issues. In particular, they need to provide adaptive mechanisms to autonomously guide the choice of operators during the search. In the case of memetic algorithms, the choice is among a set of memes, which are generally local search heuristics. In the case of hyper-heuristics, the choice may involve different types of heuristics, such as constructive heuristics, mutational heuristics or neighborhood moves, crossovers and local search heuristics. Both algorithmic schemes require mechanisms for assigning rewards to operators according to their past performance and select which operator to apply at each decision point according to the computed qualities. These mechanisms have been also studied within the evolutionary computation community using the term Adaptive Operator Selection (Fialho et al., 2010).The first paper, “Estimating Meme Fitness in Adaptive Memetic Algorithms for Combinatorial Problems” by J. Smith studies two fundamental issues when assigning credit to search operators. First, whether it is better to assign credit to a meme based on an estimate of the extreme, or the mean benefit it causes. It has been found that, when the operator choice is related to mutation in a standard evolutionary algorithm, “extremal” versions that reward occasional large jumps rather than small steady improvements, produce better results. However, in the case of memes, which by design cause local improvement, the opposite was found in this study. The second issue concerns whether the aggregation of feedback from the search process should be global or local to some part of the solution space. Results suggest that local reward schemes outperform their global counterparts in combinatorial spaces, in contrast to continuous spaces. This study therefore confirms that the performance of credit assignment mechanisms depends on both the nature of the search space and the type of search operator.The paper “Hyper-Heuristics with Low Level Parameter Adaptation” by Z. Ren, H. Jiang, J. Xuan, and Z. Luo incorporates a search-based mechanism for adapting the parameters of the low-level heuristics in a hyper-heuristic framework. Traditionally, selective hyper-heuristics adaptively select the choice of fixed low-level heuristics. But clearly, some of these heuristics are parameterised (for example, the rate of a mutation operator). The proposed framework, then, simultaneously adapt the choice of low-level heuristics and their parameters, with improved results. It also proposes a mechanisms to separate the low-level heuristics into intensification and diversification heuristics, which helps to reduce the heuristic search space and improves efficiency.Parameter tuning of evolutionary algorithms is attracting more and more interest. In particular, the Sequential Parameter Optimization (SPO) is an established parameter tuning framework (Bartz-Beielstein et al., 2005). It uses the available budget (e.g., number of function evaluations) sequentially. Information from the exploration of the search space guides the search by building meta models. New design points are determined based on predictions from these meta models. The meta models are refined stepwise to improve knowledge about the search space. SPO provides techniques to cope with noise and guarantees comparable confidence for search points. It collects information to learn from this tuning process, e.g., integrated exploratory data analysis and provides mechanisms both for interactive and automated tuning. The following two papers discuss essential ways to improve SPO related algorithms by embedding transformations and resampling techniques. Their results are in no way restricted to parameter tuning or SPO.Since data from optimization runs are non-normal, transformations are tools of choice. The paper “On the Effect of Response Transformations in Sequential Parameter Optimization,” by T. Wagner and S. Wessing enhances the SPO framework by introducing transformation steps before the actual modeling. Based on design-of-experiments techniques, they analyze the effect of integrating different transformations. They demonstrate that in particular a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicate that the rank and the Box-Cox transformation are able to improve the properties of the result distributions with respect to symmetry and normality of the residuals.The paper “Resampling Methods for Meta-Model Validation, with Recommendations for Evolutionary Computation” by B. Bischl, O. Mersmann, H. Trautmann, and C. Weihs summarizes basic resampling methods from statistics, puts them into the context of meta-model validation and extensively discusses their advantages and disadvantages together with common pitfalls users shall avoid. Meta-model validation is then discussed as a supportive technique within evolutionary algorithms, also providing some concrete examples.Finally, the paper “An Experimental Approach to the Comparison of Continuous Metaheuristics Based on Landscape Topology” by R. Morgan and M. Gallagher extends previous work of the authors on Max-Set of Gaussians (MSG) problem generators. Two Estimation of Distribution type Evolutionary Algorithms (EDA) with different abilities to adapt to problem properties are compared on various randomly determined ridge landscapes, which are constructed by means of a modification of the MSG generator. The article also suggests two visualization tools that shall be helpful for the experimental analysis of non-deterministic optimization algorithms: heatmaps and parameterized difference plots. After detecting typical landscapes that favor either one or the other algorithm, the authors undertake a meta-search in the problem parameter space, maximizing the performance difference of the algorithms, thereby further enhancing the algorithm-problem interaction knowledge for this case.The guest editors wish to thank the contributing authors for their interesting submissions and the reviewers for their constructive feedback and detailed comments. We hope this special issue will promote the cross-fertilisation of ideas in assessing the performance and designing more autonomous and user-friendly heuristic search algorithms.

The complex, often redundant and noisy data in real-world data mining (DM) applications frequently lead to inferior results when out-of-the-box DM models are applied. A tuning of parameters is essential to achieve high-quality results. In this work we aim at tuning parameters of the preprocessing and the modeling phase conjointly. The framework TDM (Tuned Data Mining) was developed to facilitate the search for good parameters and the comparison of different tuners. It is shown that tuning is of great importance for high-quality results. Surrogate-model based tuning utilizing the Sequential Parameter Optimization Toolbox (SPOT) is compared with other tuners (CMA-ES, BFGS, LHD) and evidence is found that SPOT is well suited for this task. In benchmark tasks like the Data Mining Cup (DMC) tuned models achieve remarkably better ranks than their untuned counterparts.

The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches, e.g. neuro-evolution and Sequential Parameter Optimization (SPO). In comparison to NN, GP yields superior results. By optimizing GP parameters, GP runtime can be significantly reduced without degrading result quality. The SPO-based parameter tuning leads to results with significantly lower standard deviation as compared to the GA based parameter tuning. Our methodology can be transferred to other optimization and simulation problems, where complex models have to be tuned.

Biogas plants are reliable sources of energy based on renewable materials including organic waste. There is a high demand from industry to run these plants efficiently, which leads to a highly complex simulation and optimization problem. A comparison of several algorithms from computational intelligence to solve this problem is presented in this study. The sequential parameter optimization was used to determine improved parameter settings for these algorithms in an automated manner. We demonstrate that genetic algorithm and particle swarm optimization based approaches were outperformed by differential evolution and covariance matrix adaptation evolution strategy. Compared to previously presented results, our approach required only one tenth of the number of function evaluations.

This paper presents results from extensive simulation studies on the iterated prisoner’s dilemma. Two models were implemented: a nongroup model in order to study fundamental principles of cooperation and a model to imitate ethnocentrism. Some extensions of Axelrod’s elementary model implemented individual reputation. We furthermore introduced group reputation to provide a more realistic scenario. In an environment with group reputation the behavior of one agent will affect the reputation of the whole group and vice-versa. While kind agents (e.g. those with a cooperative behavior) lose reputation when being in a group, in which defective strategies are more common, agents with defective behavior on the other hand benefit from a group with more cooperative strategies. We demonstrate that group reputation decreases cooperation with the in-group and increases cooperation with the out-group.

In less-than-truckload terminals arriving trucks have to be allocated to a gate and to a time slot for unloading. The allocation to a specific gate results in different transportation volumes for the forklift trucks inside of the terminal, depending on the destinations of the truck’s loads. While minimizing these transports the time for trucks waiting to be ordered to a gate also has to be minimized. For the first time this problem has been tackled as a 2-objective optimization problem and was solved by an (1+1)-evolution strategy. We developed a model which is derived from real freight forwarder’s data and represents a small company’s terminal on an average workday.

The Prisoner's Dilemma and the Public Goods Game are models to study mechanisms leading to the evolution of cooperation. From a simplified rational and egoistic perspective there should be no altruistic cooperation in these games at all. Previous studies observed circumstances under which cooperation can emerge. This paper demonstrates that high-order punishment opportunities can maintain a higher cooperation level in an agent based simulation of the evolution of cooperation.

Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. To demonstrate its flexibility, three scenarios are discussed: (1) no experience how to choose the parameter setting of an algorithm is available, (2) a comparison with other algorithms is needed, and (3) an optimization algorithm has to be applied effectively and efficiently to a complex real-world optimization problem. Although sequential parameter optimization relies on enhanced statistical techniques such as design and analysis of computer experiments, it can be performed algorithmically and requires basically the specification of the relevant algorithm's parameters.

The optimization of complex real-world problems might benefit from well tuned algorithm's parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis, design and analysis of computer experiments methods, and tree-based regression. It can also be applied to analyze the influence of different operators or to compare the performance of different algorithms. An evolution strategy and a simulated annealing algorithm that optimize an elevator supervisory group controller system are used to demonstrate the applicability of our approach to real-world optimization problems.

During the last decade, numerous heuristic search methods for solving multi-objective optimization problems have been developed. Population oriented approaches such as evolutionary algorithms and particle swarm optimization can be distinguished into the class of archive-based algorithms and algorithms without archive. While the latter may lose the best solutions found so far, archive based algorithms keep track of these solutions. In this article, a new particle swarm optimization technique, called DOPS, for multi-objective optimization problems is proposed. DOPS integrates well-known archiving techniques from evolutionary algorithms into particle swarm optimization. Modifications and extensions of the archiving techniques are empirically analyzed and several test functions are used to illustrate the usability of the proposed approach. A statistical analysis of the obtained results is presented. The article concludes with a discussion of the obtained results as well as ideas for further research.

We demonstrate the applicability of evolutionary algorithms (EAs) to the optimization of circuit designs. We examine the design of a full-adder cell, and show the capability of design of experiments (DOE) methods to improve the parameter-settings of EAs.

Threshold selection-a selection mechanism for noisy evolutionary algorithms-is put into the broader context of hypothesis testing. Optimal selection thresholds were derived theoretically. These theoretical results were used to find threshold values for a simple model of stochastic search and for a simplified elevator simulator. Design of experiments methods are used to validate the significance of the results.

The starting point for the analysis and experiments presented in this paper is a simplified elevator control problem, called 'S-ring'. As in many other real-world optimization problems, the exact fitness function evaluation is disturbed by noise. Evolution strategies (ES) can generally cope with noisy fitness function values. It has been proposed that the 'plus'-strategy can find better solutions by keeping over-valued function values, thus preventing inferior offspring with fitness inflated by noise from being accepted. The 'plus'-strategy builds an implicit barrier around the current best population. We propose to make this barrier building process explicit and to employ a threshold value /spl tau/ to be used in a selection operator for noisy fitness functions. 'Thresholding' accepts a new individual if its apparent fitness is better than that of the parent by at least the margin /spl tau/. First analytical investigations and empirical results from tests on the sphere-model and 'S-ring' are presented.