On the Simpson index for the Wright–Fisher process with random selection and immigration

Moran or Wright–Fisher processes are probably the most well known models to study the evolution of a population under environmental various effects.Our object of study will be the Simpson index which measures the level of diversity of the population,one of the key parameters for ecologists who study for example,forest dynamics.Following ecological motivations,we will consider,here,the case,where there are various species with fitness and immigration parameters being random processes(and thus time evolving).The Simpson index is difficult to evaluate when the population is large,except in the neutral(no selection)case,because it has no closed formula.Our approach relies on the large population limit in the“weak”selection case,and thus to give a procedure which enables us to approximate,with controlled rate,the expectation of the Simpson index at fixed time.We will also study the long time behavior(invariant measure and convergence speed towards equilibrium)of the Wright–Fisher process in a simplified setting,allowing us to get a full picture for the approximation of the expectation of the Simpson index.

Investigation Effects of Selection Mechanisms for Gravitational Search Algorithm

The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solution for the optimization problems by using interaction in all agents or masses in the population. This paper proposes and analyzes fitness-based proportional (rou- lette-wheel), tournament, rank-based and random selection mechanisms for choosing agents which they act masses in the GSA. The proposed methods are applied to solve 23 numerical benchmark functions, and obtained results are compared with the basic GSA algorithm. Experimental results show that the proposed methods are better than the basic GSA in terms of solution quality.

Temperature-controlled mode selection of Er-doped random fiber laser with disordered Bragg gratings

In this paper, we proposed a way to realize an Er-doped random fiber laser(RFL) with a disordered fiber Bragg grating(FBG) array, as well as to control the lasing mode of the RFL by heating specific locations of the disordered FBG array. The disordered FBG array performs as both the gain medium and random distributed reflectors, which together with a tunable point reflector form the RFL. Coherent multi-mode random lasing is obtained with a threshold of between 7.5 and 10 mW and a power efficiency between 23% and 27% when the reflectivity of the point reflector changes from 4% to 50%. To control the lasing mode of random emission, a specific point of the disordered FBG array is heated so as to shift the wavelength of the FBG(s) at this point away from the other FBGs.Thus, different resonance cavities are formed, and the lasing mode can be controlled by changing the location of the heating point.

Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.

Instance-Specific Algorithm Selection via Multi-Output Learning

Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.