Basic aspects of evolution strategies

Evolution strategies (ESs) are a special class of probabilistic, direct, global optimization methods. They are similar to genetic algorithms but work in continuous spaces and have the additional capability of self-adapting their major strategy parameters. This paper presents the most important features of ESs, namely their self-adaptation, as well as their robustness and potential for parallelization which they share with other evolutionary algorithms.Besides the early (1 + 1)-ES and its underlying theoretical results, the modern ( + )-ES and (, )-ES are presented with special emphasis on the self-adaptation of strategy parameters, a mechanism which enables the algorithm to evolve not only the object variables but also the characteristics of the probability distributions of normally distributed mutations. The self-adaptation property of the algorithm is also illustrated by an experimental example.The robustness of ESs is demonstrated for noisy fitness evaluations and by its application to discrete optimization problems, namely the travelling salesman problem (TSP).Finally, the paper concludes by summarizing existing work and general possibilities regarding the parallelization of evolution strategies and evolutionary algorithms in general.     

Exploration to Foster Career Development

Responses to the Adult Career Concerns Inventory (ACCI: Super, Thompson, Lindeman, Myers, & Jordaan, 1988) were organized through cluster analysis to identify different ways in which adults use exploratory behavior to cope with career development tasks. Significant differences in life-role salience among the types of adult career explorers identified were also investigated. Findings indicated a variety of ways in which adults use exploratory behavior. However, no significant differences were found in life-role salience among the types of career explorers. The discussion explains how career counselors can interpret their clients' ACCI profiles to select appropriate career interventions.     

Interne und externe Coachings in Unternehmen

When organizations employ coaching as an instrument for personnel development, the question arises whether they should opt to engage internal or external consultants. In a first step, this article identifies the characteristics of internal and external coaches by means of a series of criteria. It then goes on to discuss the factors which influence the suitability of choice between an internal or external coach at the level of the organization, in terms of the coachee and in terms of the coach. It becomes apparent that there is a situational consideration of the advantages and disadvantages. The article concludes with an actual example which demonstrates possibilities to combine the advantages of internal and external coaching.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

Putting Process (Back) In: Research on the Internationalization Process of the Firm

This paper provides a critique of existing research on the internationalization process of the firm and proposes an agenda for future inquiry. In recent years, process approaches have received increasing attention in management research, leading to a more refined understanding of the distinction between process and variance paradigms. We apply a process lens to a well‐established sub‐field of international business, namely the internationalization process of the firm. We review how this research tradition has evolved over four decades. The review commences with a reassessment of the seminal ‘stage models’ that date back to the 1970s. It then proceeds to classify subsequent research on the basis of whether it includes process data and/or process theorizing. It is found that the majority of studies in this review do not combine process data with process theorizing. We show how, even in studies that contain some process elements, a process approach is not always sustained throughout the paper. On the basis of this review, six research themes are proposed, which would form the basis for a process agenda for future research.     

Using Panel Data to Understand the Dynamics of Human Behavior in Response to Flooding

Insights into the dynamics of human behavior in response to flooding are urgently needed for the development of effective integrated flood risk management strategies, and for integrating human behavior in flood risk modeling. However, our understanding of the dynamics of risk perceptions, attitudes, individual recovery processes, as well as adaptive (i.e., risk reducing) intention and behavior are currently limited because of the predominant use of cross-sectional surveys in the flood risk domain. Here, we present the results from one of the first panel surveys in the flood risk domain covering a relatively long period of time (i.e., four years after a damaging event), three survey waves, and a wide range of topics relevant to the role of citizens in integrated flood risk management. The panel data, consisting of 227 individuals affected by the 2013 flood in Germany, were analyzed using repeated-measures ANOVA and latent class growth analysis (LCGA) to utilize the unique temporal dimension of the data set. Results show that attitudes, such as the respondents’ perceived responsibility within flood risk management, remain fairly stable over time. Changes are observed partly for risk perceptions and mainly for individual recovery and intentions to undertake risk-reducing measures. LCGA reveal heterogeneous recovery and adaptation trajectories that need to be taken into account in policies supporting individual recovery and stimulating societal preparedness. More panel studies in the flood risk domain are needed to gain better insights into the dynamics of individual recovery, risk-reducing behavior, and associated risk and protective factors.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.     

Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Statistics and Computing - We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with nonlinear...     

Participation in Everyday Life Before and After a Housing Adaptation

Housing adaptation aims to enable clients to live independently in their own homes. Studies focusing on participation in everyday life following a housing adaptation are lacking and needed. This study aimed to explore housing adaptation clients' experiences of participation in everyday life before and after a housing adaptation, through the lens of a housing adaptation, using a qualitative follow-up design, with 11 participants. It was found that when the housing adaptation met the participants' needs, performance of activities improved and the housing adaptation opened doors to engagement and participation in everyday life. Thus, focus on performance and engagement in everyday life at the onset of the housing adaptation process, combined with regular follow-ups, may enhance participation.