9,374 research outputs found

    Analyzing Adaptive Parameter Landscapes in Parameter Adaptation Methods for Differential Evolution

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    Since the scale factor and the crossover rate significantly influence the performance of differential evolution (DE), parameter adaptation methods (PAMs) for the two parameters have been well studied in the DE community. Although PAMs can sufficiently improve the effectiveness of DE, PAMs are poorly understood (e.g., the working principle of PAMs). One of the difficulties in understanding PAMs comes from the unclarity of the parameter space that consists of the scale factor and the crossover rate. This paper addresses this issue by analyzing adaptive parameter landscapes in PAMs for DE. First, we propose a concept of an adaptive parameter landscape, which captures a moment in a parameter adaptation process. For each iteration, each individual in the population has its adaptive parameter landscape. Second, we propose a method of analyzing adaptive parameter landscapes using a 1-step-lookahead greedy improvement metric. Third, we examine adaptive parameter landscapes in PAMs by using the proposed method. Results provide insightful information about PAMs in DE.Comment: This is an accepted version of a paper published in the proceedings of GECCO 202

    Screening St. Augustinegrass For USDA Zone 7

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    St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] generally has poor cold tolerance yet excellent shade tolerance. As mostly hot summers follow cold winters in USDA Hardiness Zone 7, severely damaging tall fescue [Festuca arundineacea Schreb.] and centipedegrass [Eremochloa ophiuroides (Munro) Hack.], a St. Augustinegrass cultivar cold tolerant enough to be grown for shady lawns would greatly benefit both home owners and sod growers in USDA Hardiness Zone 7. Eight St. Augustinegrass samples were selected, including industry standards \u27Raleigh\u27 and \u27Palmetto\u27, for further testing from an established germplasm collection of material collected from lawns grown in USDA Hardiness Zone 7. Morphological differences, establishment rates, shade tolerance, and most importantly cold tolerance were evaluated through field trials, greenhouse trials, and growth chamber trials. When applicable experimental samples were compared to industry standards to determine either similar or improved performance. The studies revealed several germplasm samples with differences compared to industry standards indicating possible increased performance capabilities. These findings warrant further investigation and possible DNA testing to determine genetic differences

    Modelos Bayesianos gráficos jerárquicos en psicología

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    El mejoramiento de los métodos gráficos en la investigación en psicología puede promover su uso y una mejor compresión de su poder de expresión. La aplicación de modelos Bayesianos gráficos jerárquicos se ha vuelto más frecuente en la investigación en psicología. El objetivo de este trabajo es introducir sugerencias para el mejoramiento de los modelos Bayesianos gráficos jerárquicos en psicología. Este conjunto de sugerencias se apoya en la descripción y comparación entre los dos enfoques principales con el uso de notación y pictogramas de distribución. Se concluye que la combinación de los aspectos relevantes de ambos puede mejorar el uso de los modelos Bayesianos gráficos jerárquicos en psicología.The improvement of graphical methods in psychological research can promote their use and a better comprehension of their expressive power. The application of hierarchical Bayesian graphical models has recently become more frequent in psychological research. The aim of this contribution is to introduce suggestions for the improvement of hierarchical Bayesian graphical models in psychology. This novel set of suggestions stems from the description and comparison between two main approaches concerned with the use of plate notation and distribution pictograms. It is concluded that the combination of relevant aspects of both models might improve the use of powerful hierarchical Bayesian graphical models in psychology.Fil: Campitelli, Guillermo Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Edith Cowan University; AustraliaFil: Macbeth, Guillermo Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Educación; Argentin

    Pathways to climate adapted and healthy low income housing

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    AbstractThis report presents the findings from the “Pathways to Climate Adapted and Healthy Low Income Housing” project undertaken by the CSIRO Climate Adaptation Flagship in partnership with two organisations responsible for providing social housing in Australia.The project was based on the premise that interactions between people, housing, and neighbourhood are dynamic and best viewed as a complex, dynamic social-ecological system. Using social housing as a case study, the objectives of the project were to:Model vulnerability of housing and tenants to selected climate change impacts;Identify/evaluate engineering, behavioural and institutional adaptation options;Scope co-benefits of climate adaptation for human health and well-being; andDevelop house typologies and climate analogues for national generalisations.This project was developed with the rationale that a multi-level focus on the cross-scale interactions between housing, residents, neighbourhood, and regional climate was vital for understanding the nature of climate change vulnerability and options for adaptation. The climate change hazards that were explored were increasing temperatures and more frequent and severe heatwaves in the context of heat-related health risks to housing occupants, and changes in radiation, humidity, and wind, in relation to material durability and service life of housing components and the implications for maintenance.Please cite as:Barnett G, Beaty RM, Chen D, McFallan S, Meyers J, Nguyen M, Ren Z, Spinks A, and Wang, X 2013 Pathways to climate adapted and healthy low income housing, National Climate Change Adaptation Research Facility, Gold Coast, pp. 110.This report presents the findings from the \u27Pathways to Climate Adapted and Healthy Low Income Housing\u27 project undertaken by the CSIRO Climate Adaptation Flagship in partnership with two organisations responsible for providing social housing in Australia.The project was based on the premise that interactions between people, housing, and neighbourhood are dynamic and best viewed as a complex, dynamic social-ecological system. Using social housing as a case study, the objectives of the project were to:Model vulnerability of housing and tenants to selected climate change impacts;Identify/evaluate engineering, behavioural and institutional adaptation options;Scope co-benefits of climate adaptation for human health and well-being; andDevelop house typologies and climate analogues for national generalisations.This project was developed with the rationale that a multi-level focus on the cross-scale interactions between housing, residents, neighbourhood, and regional climate was vital for understanding the nature of climate change vulnerability and options for adaptation. The climate change hazards that were explored were increasing temperatures and more frequent and severe heatwaves in the context of heat-related health risks to housing occupants, and changes in radiation, humidity, and wind, in relation to material durability and service life of housing components and the implications for maintenance

    A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

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    Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.This work was supported by grants from the Spanish Ministry of Science (TIN2016-8113-R, TIN2017-89517-P and TIN2017-83132-C2- 2-R) and Universidad Politécnica de Madrid (PINV-18-XEOGHQ-19- 4QTEBP). Eneko Osaba and Javier Del Ser-would also like to thank the Basque Government for its funding support through the ELKARTEK and EMAITEK programs. Javier Del Ser-receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government
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