Evaluation of universal, indicated, and combined cognitive-behavioral approaches to the prevention of depression among adolescents

A cluster, stratified randomized design was used to evaluate the impact of universal, indicated, and combined universal plus indicated cognitive-behavioral approaches to the prevention of depression among 13- to 15-year-olds initially reporting elevated symptoms of depression. None of the intervention approaches differed significantly from a no-intervention condition or from each other on changes in depressive symptoms, anxiety, externalizing problems, coping skills, and social adjustment. All high-symptom students, irrespective of condition, showed a significant decline in depressive symptoms and improvement in emotional well-being over time although they still demonstrated elevated levels of psychopathology compared with the general population of peers at 12-month follow-up. There were also no significant intervention effects for the universal intervention in comparison with no intervention for the total sample of students in those conditions.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.