Two groups of 30 randomly selected male students in a state industrial school for youthful offenders were tested with a battery of physiological and psychological measures administered by an exercise physiologist and psychometrist, respectively. The experimental group received a systematic physical fitness program delivered by counselors for iVa hours a day, 3 days a week, for 20 weeks. The treatment included a counseling model used previously with delinquent adolescents. At the end of the treatment period all students were posttested with the same batteries of tests. Multivariate analysis of data using Hotelling's T 2 revealed significant differences between the groups on pretest measures in favor of controls. Significant differences on the posttest measures were found in favor of experimental students. Univariate analysis identified the areas of difference both physiologically and psychologically.

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in Cþþ, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results.

Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but e ective strategies can be found. CCS CONCEPTS•Computing methodologies → Arti cial intelligence; Model development and analysis; KEYWORDSGames, Genetic programming, Image analysis, Arti cial intelligence BACKGROUNDWhile game playing in the ALE involves both image processing and reinforcement learning techniques, research on these topics using CGP has not been equal. ere is a wealth of literature concerning image processing in CGP, but li le concerning reinforcement learning. Here, we therefore focus on the general history of CGP and its application to image processing.

Coastal development and urban planning are facing different issues including natural disasters and extreme storm events. The ability to track and forecast the evolution of the physical characteristics of coastal areas over time is an important factor in coastal development, risk mitigation and overall coastal zone management. Traditional bathymetry measurements are obtained using echo-sounding techniques which are considered expensive and not always possible due to various complexities. Remote sensing tools such as satellite imagery can be used to estimate bathymetry using incident wave signatures and inversion models such as physical models of waves. In this work, we present two novel approaches to bathymetry estimation using deep learning and we compare the two proposed methods in terms of accuracy, computational costs, and applicability to real data. We show that deep learning is capable of accurately estimating ocean depth in a variety of simulated cases which offers a new approach for bathymetry estimation and a novel application for deep learning.

Optimizing a wind farm layout is a very complex problem that involves many local and global constraints such as interturbine wind interference or terrain peculiarities. Existing methods are either inefficient or, when efficient, take days or weeks to execute. Solutions are contextually sensitive to the specific values of the problem variables; when one value is modified, the algorithm has to be re-run from scratch. This paper proposes the use of a developmental model to generate farm layouts. Controlled by a gene regulatory network, virtual cells have to populate a simulated environment that represents the wind farm. When the cells' behavior is learned, this approach has the advantage that it is re-usable in different contexts; the same initial cell is responsive to a variety of environments and the layout generation takes few minutes instead of days.