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In the era of e-commerce, accurately predicting user purchase intentions has become a crucial factor for enhancing sales efficiency and optimizing the customer experience. Addressing the limitations of traditional ensemble strategies, which often suffer from subjective biases during the model design phase, this paper introduced an adaptive evolutionary ensemble learning model to predict user purchase intentions. This model adaptively selected the optimal base learners and meta-learners, incorporating both the predictive information from the base learners and the differential information between features to expand the feature dimensions, enhancing prediction accuracy. Moreover, to further refine the predictive capabilities of the model, this paper designed a binary adaptive differential evolution algorithm for feature selection, aiming to identify features that significantly influence the prediction outcome. Research results show that the binary adaptive differential evolution algorithm outperforms traditional optimization algorithms in global searches and feature selection. Compared to six common ensemble models and the DeepForest model, the proposed evolutionary ensemble model achieves a 2.76% and 2.72% increase in AUC value, respectively, and effectively mitigates the impacts of data imbalance [ABSTRACT FROM AUTHOR]

Accurate and reliable prediction of PM2.5 concentrations is the basis for appropriate warning measures, and a single prediction model is often ineffective. In this paper, we propose a novel decomposition-and-ensemble model to predict the concentration of PM2.5. The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to decompose PM2.5 series, Support Vector Regression (SVR) to predict each Intrinsic Mode Function (IMF), and a hybrid algorithm based on Differential Evolution (DE) and Grey Wolf Optimizer (GWO) to optimize SVR parameters. The proposed prediction model EEMD-SVR-DEGWO is employed to forecast the concentration of PM2.5 in Guangzhou, Wuhan, and Chongqing of China. Compared with six prediction models, the proposed EEMD-SVR-DEGWO is a reliable predictor and has achieved competitive results. [ABSTRACT FROM AUTHOR]

Understanding and forecasting air quality index (AQI) plays a vital role in guiding the reduction of air pollution and helping social sustainable development. By combining fuzzy logic with decomposition techniques, ANFIS has become an important means to analyze the data resources, uncertainty and fuzziness. However, few studies have paid attention to the noise of decomposed subseries. Therefore, this paper presents a novel decomposition-denoising ANFIS model named SSADD-DE-ANFIS (Singular Spectrum Analysis Decomposition and Denoising-Differential Evolution-Adaptive Neuro-Fuzzy Inference System). This method uses twice SSA to decompose and denoise the AQI series, respectively, then fed the subseries obtained after the decomposition and denoising into the constructed ANFIS for training and predicting, and the parameters of ANFIS are optimized using DE. To investigate the prediction performance of the proposed model, twelve models are included in the comparisons. The experimental results of four seasons show that: the RMSE of the proposed SSADD-DE-ANFIS model is 1.400628, 0.63844, 0.901987 and 0.634114, respectively, which is 19.38%, 21.27%, 20.43%, 21.27% and 87.36%, 88.12%, 88.97%, 88.71% lower than that of the single SSA decomposition and SSA denoising. Diebold-Mariano test is performed on all the prediction results, and the test results show that the proposed model has the best prediction performance. [ABSTRACT FROM AUTHOR]

In the field of mobile App just-in-time ( JIT) defect prediction, most studies only focus on the performance of the prediction model but ignore the interpretability of the model. Therefore, developers will lack trust in the prediction results of the defect prediction model and hinder the application of the defect prediction model in practice. This paper studied the interpretability of the mobile App JIT defect prediction model. And it proposed the method ExplainApp by hyperparameter optimization of the local interpretability technology LIME method via a differential evolution algorithm, which could generate explanations for the prediction results. This study selected 14 real-world Android applications as preposed experimental objects. The final results show that, the method ExplainApp can explain the prediction results of the instance generated by the mobile A pp JIT defect prediction model. And the method ExplainApp is superior to the original LIME method in terms of the goodness of fit. Specifically, the performance can improve by 94. 50% on average. [ABSTRACT FROM AUTHOR]

Cloud computing promises elasticity, flexibility and cost-effectiveness to satisfy service level agreement conditions. The cloud service providers should plan and provision the computing resources rapidly to ensure the availability of infrastructure to match the demands with closed proximity. The workload prediction has become critical as it can be helpful in managing the infrastructure effectively. In this paper, we present a workload forecasting framework based on neural network model with supervised learning technique. An improved and adaptive differential evolution algorithm is developed to improve the learning efficiency of predictive model. The algorithm is capable of optimizing the best suitable mutation operator and crossover operator. The prediction accuracy and convergence rate of the learning are observed to be improved due to its adaptive behavior in pattern learning from sampled data. The predictive model's performance is evaluated on four real-world data traces including Google cluster trace and NASA Kennedy Space Center logs. The results are compared with state-of-the-art methods, and improvements up to 91%, 97% and 97.2% are observed over self-adaptive differential evolution, backpropagation and average-based workload prediction techniques, respectively. [ABSTRACT FROM AUTHOR]

Summary: Although neural networks have been widely applied to various hydrological problems, including river flow forecasting, for at least 15years, they have usually been trained by means of gradient-based algorithms. Recently nature inspired Evolutionary Computation algorithms have rapidly developed as optimization methods able to cope not only with non-differentiable functions but also with a great number of local minima. Some of proposed Evolutionary Computation algorithms have been tested for neural networks training, but publications which compare their performance with gradient-based training methods are rare and present contradictory conclusions. The main goal of the present study is to verify the applicability of a number of recently developed Evolutionary Computation optimization methods, mostly from the Differential Evolution family, to multi-layer perceptron neural networks training for daily rainfall–runoff forecasting. In the present paper eight Evolutionary Computation methods, namely the first version of Differential Evolution (DE), Distributed DE with Explorative–Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization and Efficient Population Utilization Strategy Particle Swarm Optimization are tested against the Levenberg–Marquardt algorithm – probably the most efficient in terms of speed and success rate among gradient-based methods. The Annapolis River catchment was selected as the area of this study due to its specific climatic conditions, characterized by significant seasonal changes in runoff, rapid floods, dry summers, severe winters with snowfall, snow melting, frequent freeze and thaw, and presence of river ice – conditions which make flow forecasting more troublesome. The overall performance of the Levenberg–Marquardt algorithm and the DE with Global and Local Neighbors method for neural networks training turns out to be superior to other Evolutionary Computation-based algorithms. The Levenberg–Marquardt optimization must be considered as the most efficient one due to its speed. Its drawback due to possible sticking in poor local optimum can be overcome by applying a multi-start approach. [Copyright &y& Elsevier]

With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system's requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system. [ABSTRACT FROM AUTHOR]

In practical applications, solving multimodal multiobjective optimization problems (MMOPs), which have multiple Pareto optimal sets (PSs) in the decision space mapping to the same Pareto front (PF) in the objective space, has great significance for decision makers. The issue of how to maintain diversity in both decision space and objective space remains a key problem for existing multimodal multiobjective evolutionary algorithms. To address this issue, a novel grey prediction evolution algorithm for multimodal multiobjective optimization, termed MMGPE, is proposed in this paper. This is the first time that the grey prediction evolution algorithm (GPE), which is a recently proposed competitive optimization algorithm with strong exploration capability, is improved for MMOPs. These improvements are conducted in the following four aspects: (1) an initialization operator based on particle swarm optimization, (2) an adaptive parameter setting strategy depending on the domain of decision variables of MMOPs, (3) an accelerating convergence mechanism inspired by the niche principle, and (4) an environmental selection operator based on a nondominated sorting mechanism and a special crowding distance approach. The performance of MMGPE is compared with two state-of-the-art multiobjective evolutionary algorithms and four multimodal multiobjective evolutionary algorithms on 11 multimodal multiobjective test functions. MMGPE is also applied to solve a practical problem. The results show the MMGPE's effectiveness and superiority in achieving the goal of finding multiple PSs while obtaining a well-distributed PF. [ABSTRACT FROM AUTHOR]