Nonparametric regression estimator based on locally weighted least squares fitting has been studied by Fan and Ruppert and Wand. The latter paper also studies, in the univariate case, nonparametric derivative estimators given by a locally weighted polynomial fitting. Compared with traditional kernel estimators, these estimators are often of simpler form and possess some better properties. In th...

Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, machine learning method based on dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve interpretability training process, an effective heuristic algorithm, i.e., covariance matrix adaptation evolution strategy (CMA-ES),...

This report describes a general method for rendering search coordinate system independent, Adaptive Encoding (AE). Adaptive Encoding is applicable to any continuous domain search algorithm and includes (incremental) changes of the coordinate system, that is, changes of the representation of solutions. One attractive way to change the representation within AE is derived from the Covariance Matri...

Randomized direct-search methods for the optimization of a function f : R → R given by a black box for f -evaluations are investigated. We consider the cumulative step-size adaptation (CSA) for the variance of multivariate zero-mean normal distributions. Those are commonly used to sample new candidate solutions within metaheuristics, in particular within the CMA Evolution Strategy (CMA-ES), a s...

Randomized direct-search methods for the optimization of a function f : R → R given by a black box for f -evaluations are investigated. We consider the cumulative step-size adaptation (CSA) for the variance of multivariate zero-mean normal distributions. Those are commonly used to sample new candidate solutions within metaheuristics, in particular within the CMA Evolution Strategy (CMA-ES), a s...

Derived from the concept of self-adaptation in evolution strategies, the CMA (Covariance Matrix Adaptation) adapts the covariance matrix of a multi-variate normal search distribution. The CMA was originally designed to perform well with small populations. In this review, the argument starts out with large population sizes, reflecting recent extensions of the CMA algorithm. Commonalities and dif...

Object detection is one of the essential tasks computer vision. detectors based on deep neural network have been used more and widely in safe-sensitive applications, like face recognition, video surveillance, autonomous driving, other tasks. It has proved that object are vulnerable to adversarial attacks. We propose a novel black-box attack method, which can successfully regression-based region...

Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create ...

The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel eomputational method for solv­ ing seareh and optimization problems with single or multiple objeetives, HEM is an intelligent evolutionary optimization method that uses eonsensus knowledge from experts with the aim of inferring the most suitable parameters to aehieve the evolution in an intelligent way, HEM is abl...

The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the ...