Self-adjusting the intensity of assortative mating in genetic algorithms

Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population. We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA.     

Application of autoregressive spectral analysis to missing data problems

Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood algorithm is presented that uses only previous observations falling in a finite interval to compute the likelihood, instead of all previous observations. The resulting nonlinear estimation algorithm requires no user-provided initial solution, is suited for order selection, and can give very accurate spectra even if less than 10% of the data remains.     

Finite sample properties of ARMA order selection

The cost of order selection is defined as the loss in model quality due to selection. It is the difference between the quality of the best of all available candidate models that have been estimated from a finite sample of N observations and the quality of the model that is actually selected. The order selection criterion itself has an influence on the cost because of the penalty factor for each additionally selected parameter. Also, the number of competitive candidate models for the selection is important. The number of candidates is, of itself, small for the nested and hierarchical autoregressive/moving average (ARMA) models. However, intentionally reducing the number of selection candidates can be beneficial in combined ARMA(p,q) models, where two separate model orders are involved: the AR order p and the MA order q. The selection cost can be diminished by creating a nested sequence of ARMA(r,r-1) models. Moreover, not evaluating every combination (p,q) of the orders considerably reduces the required computation time. The disadvantage may be that the true ARMA(p,q) model is no longer among the nested candidate models. However, in finite samples, this disadvantage is largely compensated for by the reduction in the cost of order selection by considering fewer candidates. Thus, the quality of the selected model remains acceptable with only hierarchically nested ARMA(r,r-1) models as candidates.     

It takes two to mimic: Behavioral consequences of self-construals.

The present studies demonstrated the moderation of self-construal orientation on mimicry. Recent research has indicated that an interdependent self-construal is associated with assimilation of the other to the self whereas an independent self-construal is associated with minimizing the influence of others on the self (H. R. Markus & S. Kitayama, 1991; D. Stapel & W. Koomen, 2001). Therefore, the authors hypothesized that an interdependent self-construal would be associated with more mimicry than an independent self-construal. When self-construal orientations were experimentally primed. as in Studies 1 and 2, independent self-construals produced less nonconscious mimicry than interdependent self-construals. When self-construals were examined as cultural differences with either a chronically dominant independent (Americans) or interdependent (Japanese) construal of the self, these results were replicated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Critical Shear Stress of Bimodal Sediment in Sand-Gravel Rivers

A new model for the critical shear stress and the transport of graded sediment is presented. The model is based on the size distribution of the bed surface and can be used to compute sediment transport rates in numerical simulations with an active layer model. This model makes a distinction between unimodal and bimodal sediments. It is assumed that all size fractions of unimodal sediments have the same critical shear stress while there is selective transport for the gravel fractions of bimodal sediments. A recently published laboratory transport data set is used to calibrate our model.     

Creating a Student Centered Learning Environment at the University of Denver

A team of faculty members at the University of Denver changed the learning environment in key courses in the Department of Engineering from predominately teacher centered to student centered. Through this funded project new grading methods were implemented, classrooms were renovated and wired with studio layouts to facilitate learning, the Engineering Circuits Laboratory was rewired and instrumented for automated data acquisition and reporting, and two new pedagogical approaches were developed. At the onset of the project, six goals were established related to student learning. The introduction of industry standard hardware and software provided students with unprecedented hands‐on experience and project related activities stimulated faculty innovations in other current and future courses. Assessment results indicate that the new grading system improved the clarity of expectations for students before assignments were given resulting in increased reported motivation for learning in many courses. Even though course GPAs did not always reflect higher achievement on graded work, faculty members firmly believe that deeper understanding was achieved because more complex material was assimilated.     

Treatment of essential arterial hypertension with hypercholesterolemia. Effects of an alpha-adrenergic blocker and an inhibitor of the angiotensin converting enzyme

BACKGROUND: Hypertension and hypercholesterolemia are frequently associated with this leading to considerable cardiovascular risk. METHODS: An open parallel randomized study was performed in which the effects of doxazosin, an alpha-adrenergic blocker and enalapril, an inhibitor of the angiotensin converting enzyme were compared in 70 patients with essential high blood pressure and plasma cholesterol levels greater than 240 mg/dl. Following 2-4 weeks of placebo administration the patients were randomly treated with one of the two drugs. When required doses were increased and hydrochlorothiazide added until blood pressure lower than 160/95 mmHg was achieved. After this period the patients were observed for a minimum of 8 weeks. The mean length of the study was of 22 weeks. RESULTS: Both drugs significantly reduced blood pressure without modifying cardiac frequency. Doxazosin tended to favorably modify the lipid profile of the plasma while enalapril significantly reduced the levels of cholesterol, lipids and high density lipoproteins (HDL). Upon termination of the study the total HDL/cholesterol index increased 8.6% in those treated with doxazosin and decreased 5.5% in those receiving enalapril (p < 0.05). CONCLUSIONS: Although doxazosin and enalapril are potent antihypertensive drugs, the effects on plasma lipid obtained with doxazosin indicate that a reduction in cardiovascular risk was achieved with this drug in the patients included in this study.