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Quality Analysis of Discretization Methods for Estimation of Distribution algorithms

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2014年第5期E97D卷 1312-1323页

作者： Chen, Chao-Hong Chen, Ying-ping Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

Estimation of distribution algorithms (EDAs). since they were introduced, have been successfully used to solve discrete optimization problems and hence proven to be an effective methodology for discrete optimization. To enhance the applicability of EDAs, researchers started to integrate EDAs with discretization methods such that the EDAs designed for discrete variables can be made capable of solving continuous optimization problems. In order to further our understandings of the collaboration between EDAs and discretization methods, in this paper, we propose a quality measure of discretization methods for EDAs. We then utilize the proposed quality measure to analyze three discretization methods: fixed-width histogram (FWH). fixed-height histogram (FHH), and greedy random split (GRS). Analytical measurements are obtained for FHH and FWH, and sampling measurements are conducted for FHH. FWH, and GRS. Furthermore, we integrate bayesian optimization algorithm (BOA), a representative EDA, with the three discretization methods to conduct experiments and to observe the performance difference. A good agreement is reached between the discretization quality measurements and the numerical optimization results. The empirical results show that the proposed quality measure can be considered as an indicator of the suitability for a discretization method to work with EDAs.

关键词： quality analysis discretization distortion fixed-width histogram fixed-height histogram greedy random split estimation of distribution algorithm bayesian optimization algorithm

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Getting the best of both worlds: Discrete and continuous genetic and evolutionary algorithms in concert

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INFORMATION SCIENCES 2003年第3-4期156卷 147-171页

作者： Pelikan, M Goldberg, DE Tsutsui, S Univ Illinois Illinois Genet Algorithms Lab Urbana IL 61801 USA

This paper describes an evolutionary algorithm for optimization of continuous problems that combines advanced recombination techniques for discrete representations with advanced mutation techniques for continuous representations. Discretization is used to transform solutions between the discrete and continuous domains. The proposed algorithm combines the strengths of purely continuous and purely discrete approaches and eliminates some of their disadvantages. The paper tests the proposed algorithm with the recombination operator of the bayesian optimization algorithm, sigma-self-adaptive mutation, and three discretization methods. The empirical results on three problems suggest that the tested variant of the algorithm scales up well on all tested problems, indicating good scalability over a broad range of continuous problems. (C) 2003 Elsevier Inc. All rights reserved.

关键词： bayesian optimization algorithm evolution strategies adaptive mutation linkage learning continuous optimization

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Research on mine water source classifications based on BO-CatBoost

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第10期196卷 876-876页

作者： Li, Han Yang, Zhenwei Lv, Hang Wang, Man Zhang, Bo Yan, Hengrui Zhang, Hongyu Henan Polytech Univ Inst Resources & Environm Jiaozuo 454000 Peoples R China Collaborat Innovat Ctr Coal Work Safety & Clean Hi Jiaozuo 454000 Peoples R China China Pingmei Shenma Holding Grp Co Ltd Pingdingshan 467000 Henan Peoples R China

Mine water surge is one of the main safety risks in coal mines. This research offers a novel mine water source identification model (BO-CatBoost) to successfully avoid and control mine sudden water catastrophes by properly identifying the sources of mine water. First, the classification model is trained and built using the Categorical Boosting (CatBoost) algorithm. The Gaussian process bayesian optimization (BO) algorithm is used to optimize parameters, and the optimal parameter combination is integrated into the CatBoost algorithm to build the BO-CatBoost mine water source identification model, which further improves the accuracy of mine water source identification. The model was also applied to the Pingdingshan mine to verify the practicality of the model. Then, 29 groups of unknown water sources in Pingdingshan were selected as validation samples for the model and compared with the conventional CatBoost, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (Xgboost) models. The comparison results demonstrate that the accuracy of LightGBM, Xgboost, CatBoost, and BO-CatBoost models can reach 69%, 79.3%, 79.3%, and 100% respectively, and the RMSE is 0.947, 0.643, 0.719, and 0.0 respectively. The comprehensive analysis shows that, when it comes to mine water source detection, the BO-CatBoost model performs noticeably better than other models in terms of discriminative accuracy and generalization capacity. Lastly, the multi-output prediction and decision-making process of the BO-CatBoost water source identification model is visualized by the interpretability analysis performed with the SHAP approach. The research demonstrates that the BO-CatBoost model can more precisely and impartially identify mine water sources, offering fresh concepts for mine water source detection.

关键词： Mine water source Pingdingshan coalfield CatBoost SHAP bayesian optimization algorithm

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Entropy-based efficiency enhancement techniques for evolutionary algorithms

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INFORMATION SCIENCES 2012年 188卷 100-120页

作者： Hoang Ngoc Luong Hai Thi Thanh Nguyen Ahn, Chang Wook Sungkyunkwan Univ Sch Informat & Commun Engn Seoul South Korea

This paper introduces the notion of an entropy measurement for populations of candidate solutions in evolutionary algorithms, developing both conditional and joint entropy-based algorithms. We describe the inherent characteristics of the entropy measurement and how these affect the search process. Following these discussions, we develop a recognition mechanism through which promising candidate solutions can be identified without the need of invoking costly evaluation functions. This on-demand evaluation strategy (ODES) is able to perform decision making tasks regardless of whether the actual fitness evaluation is necessary or not, making it an ideal efficiency enhancement technique for accelerating the computational process of evolutionary algorithms. Two different evolutionary algorithms, a traditional genetic algorithm and a multivariate estimation of distribution algorithm, are employed as example targets for the application of our on-demand evaluation strategy. Ultimately, experimental results confirm that our method is able to broadly improve the performance of various population-based global searchers. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithms Efficiency enhancement Evaluation relaxation Entropy measurement bayesian optimization algorithm Network coding

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Hybrid model for daily runoff interval predictions based on bayesian inference

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HYDROLOGICAL SCIENCES JOURNAL 2023年第1期68卷 62-75页

作者： Xu, Hanbing Song, Songbai Li, Jun Guo, Tianli Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Area Minist Educ Yangling Peoples R China

The highly non-linear and nonstationary nature of runoff events in changing environments makes accurate and reliable runoff forecasting difficult. We propose a hybrid model by integrating an autoregressive (AR) model, bayesian inference, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, bayesian optimization, and support vector regression. Two bayesian inference methods (the No-U-Turn Sampler (NUTS) and variational inference) were used to calculate the parameters of the AR model to obtain a bayesian AR (BAR) model. Credible intervals were used to analyse the uncertainty of the parameters and model prediction results. The above model is applied to the daily runoff predictions of hydrological stations in the Yellow River basin of China. The results show that (1) the hybrid model can improve the prediction accuracy and (2) the NUTS algorithm-based model provides a narrower reliable interval and performs better in uncertainty analyses.

关键词： bayesian inference autoregressive model support vector regression bayesian optimization algorithm daily runoff prediction Yellow River basin

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State of health estimation for lithium-ion batteries with bayesian optimized Gaussian process regression

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Ye, Li-hua He, Zhou Ke, Cheng-long Cheng, Xing Zhao, Xu Zhang, Zi-xing Shi, Ai-ping Jiangsu Univ Sch Automot & Transportat Engn 301 Xuefu Rd Zhenjiang Jiangsu Peoples R China

The estimation of the state of health (SOH) of lithium-ion batteries is of great importance to ensure the safe and stable operation of a lithium-ion battery management system (BMS). Accurate estimation of SOH can effectively prevent unnecessary losses of lithium-ion batteries due to failures. Therefore, a bayesian-optimized Gaussian process regression (BO-GPR) method is proposed for SOH estimation. Firstly, the ageing characteristics reflecting the SOH of the battery are extracted from the charge/discharge time and incremental capacity (IC) curve, and the correlation between the health characteristics and the SOH is analysed by means of grey correlation (GRA). On the other hand, a bayesian optimization algorithm is used to automatically find the optimal hyperparameters of the GPR model and trained using the battery dataset to obtain the SOH estimating model. To validate the effectiveness of the method, the datasets provided by NASA and Oxford are used as experimental objects and compared with different data-driven models to verify the accuracy and reliability of the proposed model. The experimental results show that the method proposed in this paper has a high accuracy in the estimation of the SOH, with the maximum average estimation error being within 1%.

关键词： Lithium-ion battery state of health bayesian optimization algorithm Gaussian process regression

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Optimal decoding and minimal length for the non-unique oligonucleotide probe selection problem

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NEUROCOMPUTING 2010年第13-15期73卷 2407-2418页

作者： Ghoraie, Laleh Soltan Gras, Robin Wang, Lili Ngom, Alioune Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

One of the applications of DNA microarrays is recognizing the presence or absence of different biological components (targets) in a sample. Hence, the quality of the microarrays design which includes selecting short Oligonucleotide sequences (probes) to be affixed on the surface of the microarray becomes a major issue. A good design is the one that contains the minimum possible number of probes while having an acceptable ability in identifying the targets existing in the sample. This paper focuses on the problem of computing the minimal set of probes which is able to identify each target of a sample, referred to as non-unique oligonucleotide probe selection. We present the application of an estimation of distribution algorithm (EDA) named bayesian optimization algorithm (BOA) to this problem, for the first time. The proposed approach considers integration of BOA and one simple heuristic introduced for the non-unique probe selection problem. The results provided by this approach compare favorably with the state-of-the-art methods in the single target case. While most of the recent research works on this problem has been focusing on the single target case only, we present the application of our method in integration with decoding approach in a multiobjective optimization framework for solving the problem in the case of multiple targets. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.

关键词： Microarray Probe selection Target Estimation of distribution algorithm bayesian optimization algorithm Heuristic Multiobjective optimization Decoding Markov chain Monte Carlo

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Effective search for Pittsburgh learning classifier systems via estimation of distribution algorithms

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INFORMATION SCIENCES 2012年 198卷 100-117页

作者： Yang, Jiadong Xu, Hua Jia, Peifa Tsinghua Univ State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & Technol Dept Comp Sci & Technol Beijing 100084 Peoples R China

Pittsburgh-style learning classifier systems (LCSs), in which an entire candidate solution is represented as a set of variable number of rules, combine supervised learning with genetic algorithms (GAS) to evolve rule-based classification models. It has been shown that standard crossover operators in GAs do not guarantee an effective evolutionary search in many sophisticated problems that contain strong interactions between features. In this paper, we propose a Pittsburgh-style learning classifier system based on the bayesian optimization algorithm with the aim of improving the effectiveness and efficiency of the rule structure exploration. In the proposed method, classifiers are generated and recombined at two levels. At the lower level, single rules contained in classifiers are produced by sampling bayesian networks which characterize the global statistical information extracted from the current promising rules in the search space. At the higher level, classifiers are recombined by rule-wise uniform crossover operators to keep the semantics of rules in each classifier. Experimental studies on both artificial and real world binary classification problems show that the proposed method converges faster while achieving solutions with the same or even higher accuracy compared with the original Pittsburgh-style LCSs. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Learning classifier system Genetics-based machine learning Estimation of distribution algorithm bayesian optimization algorithm Evolutionary computation

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A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6678页

作者： Zhou, Shuncheng Li, Honghui Fu, Xueliang Jiao, Yuanyuan Inner Mongolia Agr Univ Coll Comp & Informat Engn Hohhot 010018 Peoples R China

With the increasingly severe challenge of Software Supply Chain (SSC) security, the rising trend in guarding against security risks has attracted widespread attention. Existing techniques still face challenges in both accuracy and efficiency when detecting malware in SSC. To meet this challenge, this paper introduces two novel models, named the bayesian optimization-based Support Vector Machine (BO-SVM) and the Long Short-Term Memory-BO-SVM (LSTM-BO-SVM). The BO-SVM model is constructed on an SVM foundation, with its hyperparameters optimized by bayesian optimization. To further enhance its accuracy and efficiency, the LSTM-BO-SVM model is proposed, building upon BO-SVM and employing LSTM networks for pre-classification. Extensive experiments were conducted on two datasets: the balanced ClaMP dataset and the unbalanced CICMalDroid-2020 dataset. The experimental results indicate that the BO-SVM model is superior to other models in terms of accuracy;the accuracy of the LSTM-BO-SVM model on the two datasets is 98.2% and 98.6%, respectively, which is 2.9% and 2.2% higher than that of the BO-SVM on these two datasets.

关键词： software supply chain malware detection long short-term memory network bayesian optimization algorithm support vector machine

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Prediction of PM2.5 Concentration Based on bayesian optimization Random Forest

Prediction of PM2.5 Concentration Based on Bayesian Optimiza...

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第43届中国控制会议

作者： Yingji Deng Pengfei Zhi Wanlu Zhu Taize Sang Yuanjiang Li Jiangsu University of Science and Technology Jiangsu Daqo Cubicle-Type Substation Technology Co. Ltd.

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Nowadays, people pay more and more attention to air quality, and smog as a serious air pollution phenomenon has received greater attention. As the main culprit of haze phenomenon and an important indicator of air quality, it is of great significance to forecast PM2.5. Aiming at the prediction of PM2.5 concentration, this paper proposes a method of using random forest algorithm to build a regression prediction model and then predict PM2.5 concentration. This method mainly uses meteorological factors related to PM2.5(such as dew point, temperature, humidity, etc.) and historical data of PM2.5 concentration in the past three hours as input to build the model. Forecast future hourly PM2.5 concentrations. In this paper, the random forest model is successfully constructed, and the bayesian algorithm is used to optimize the random forest model, and the PM2.5 concentration is well predicted. At the same time, this paper also constructs the decision tree, support vector machine, intelligent gray Wolf algorithm optimized random forest model and Bayes algorithm optimized random forest model to compare. The superiority of bayesian algorithm in optimizing stochastic forest model is proved.

关键词： Integrated learning Random forest PM2.5 concentration prediction bayesian optimization algorithm

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