Differential evolution (DE) has been proven to be one of the most powerful global numerical optimization algorithms in evolutionary algorithm family. The core operator DE is differential mutation operator. Generally, parents are randomly chosen from current population. In nature, good species always contain information, and hence, they have more chance utilized guide other species. Inspired by this phenomenon, paper, we propose ranking-based operators for algorithm, where some proportionally...

Because learning an optimal Bayesian network classifier is NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel model: hidden (HNB). HNB, parent created for each attribute which combines influences all other attributes. We experimentally test HNB in terms of classification accuracy, using 36 UCI data sets selected by Weka, compare it to (NB), selective classifiers (SBC),...

In differential evolution (DE) studies, there are many parameter adaptation methods, aiming at tuning the mutation factor F and crossover probability CR. However, these methods still cannot resolve issues of population premature convergence stagnation. To address issues, in this paper, we investigate regarding diversity dimensional level propose a mechanism named auto-enhanced (AEPD) to automatically enhance diversity. AEPD is able identify moments when becomes converging or stagnating by...

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising quality safe drinking effective irrigation. This research primarily focused on employing Water Quality Index (WQI) gauge water’s appropriateness these purposes. However,...

KNN (k-nearest-neighbor) has been widely used as an effective classification model. In this paper, we summarize three main shortcomings confronting and single out methods for overcoming its shortcomings. Keeping to these methods, try our best survey some improved algorithms experimentally tested their effectiveness. Besides, discuss directions future study on KNN.

Differential evolution (DE) is a simple, yet efficient, evolutionary algorithm for global numerical optimization, which has been widely used in many areas. However, the choice of best mutation strategy difficult specific problem. To alleviate this drawback and enhance performance DE, paper, we present family improved DE that attempts to adaptively choose more suitable problem at hand. In addition, our proposed adaptation mechanism (SaM), different parameter methods can be strategies. order...

Differential evolution (DE) is a powerful evolutionary algorithm (EA) for numerical optimization. Combining with the constraint-handling techniques, recently, DE has been successfully used constrained optimization problems (COPs). In this paper, we propose adaptive ranking mutation operator (ARMOR) when solving COPs. The ARMOR expected to make converge faster and achieve feasible solutions faster. ARMOR, are adaptively ranked according situation of current population. More specifically,...

The ensemble-based feature selection method presents the merit of acquisition more informative and compact features than those obtained by individual methods.

As an unsupervised dimensionality reduction method, principal component analysis (PCA) has been widely considered as efficient and effective preprocessing step for hyperspectral image (HSI) processing tasks. It takes each band a whole globally extracts the most representative bands. However, different homogeneous regions correspond to objects, whose spectral features are diverse. is obviously inappropriate carry out through unified projection entire HSI. In this paper, simple but very...

It is well known that in evolutionary algorithms (EAs), different reproduction operators may be suitable for problems or running stages. To improve the algorithm performance, ensemble of multiple has become popular. Most techniques achieve this goal by choosing an operator according to a probability learned from previous experience. In contrast these techniques, paper we propose cheap surrogate model-based multioperator search strategy optimization. our approach, set candidate offspring...

MOEA/D is a recently proposed methodology of Multiobjective Evolution Algorithms that decomposes multiobjective problems into number scalar subproblems and optimizes them simultaneously. However, classical uses same weight vectors for different shapes Pareto front. We propose novel method called Pareto-adaptive (paλ) to automatically adjust the by geometrical characteristics Evaluation on confirms new algorithm obtains higher hypervolume, better convergence more evenly distributed solutions...

Finding multiple roots of nonlinear equation systems (NESs) in a single run is one the most important challenges numerical computation. We tackle this challenging task by combining strengths repulsion technique, diversity preservation mechanism, and adaptive parameter control. First, technique motivates population to find new repulsing regions surrounding previously found roots. However, as many possible, algorithm designers need address key issue: how maintain population. To end, mechanism...

In image analysis, samples are always represented by multiple view features and associated with class labels for better interpretation. However, data may include noisy, irrelevant redundant features, while can be noisy incomplete. Due to the special characteristic, it is hard perform feature selection on multi-view multi-label data. To address these challenges, in this paper, we propose a novel sparse (MSFS) method, which exploits both relations label correlations select discriminative...

Cooperative co-evolution (CC) is an explicit means of problem decomposition in multipopulation evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents a co-evolve, and are likely to have different contributions the improvement best overall solution problem. Hence, it makes sense that more computational resources should be allocated with greater contributions. In this paper, we study how allocate context subsequently propose new...

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace has been proven be powerful for exploiting intrinsic relationship between data points. Despite impressive performance in clustering, traditional subspace methods often ignore inherent structural information among In this paper, we revisit with graph convolution and present novel framework called Graph Convolutional Clustering (GCSC) robust clustering. Specifically, recasts...

Transfer learning and ensemble are the new trends for solving problem that training data test have different distributions. In this paper, we design an transfer framework to improve classification accuracy when insufficient. First, a weightedresampling method is proposed, which named TrResampling. each iteration, with heavy weights in source domain resampled, TrAdaBoost algorithm used adjust of target data. Second, three classic machine algorithms, namely, naive Bayes, decision tree, SVM, as...