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MOBOpt - multi-objective bayesian optimization

SOFTWAREX 2020年 12卷

作者： Galuzio, Paulo Paneque de Vasconcelos Segundo, Emerson Hochsteiner Coelho, Leandro dos Santos Mariani, Viviana Cocco Posit Univ Prof Pedro Viriato Parigot Souza 5300 BR-81280330 Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Imaculada Conceicao 1155 BR-80215901 Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn Cel Francisco Heraclito dos Santos 100 BR-81531980 Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Imaculada Conceicao 1155 BR-80215901 Curitiba Parana Brazil

This work presents a new software, programmed as a Python class, that implements a multiobjective bayesian optimization algorithm. The proposed method is able to calculate the Pareto front approximation of optimization problems with fewer objective functions evaluations than other methods, which makes it appropriate for costly objectives. The software was extensively tested on benchmark functions for optimization, and it was able to obtain Pareto Function approximations for the benchmarks with as many as 20 objective function evaluations, those results were obtained for problems with different dimensionalities and constraints. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： optimization problems Multi-objective optimization bayesian optimization algorithm

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Improving case-based reasoning in solving optimization problems using bayesian optimization algorithm

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INTELLIGENT DATA ANALYSIS 2012年第2期16卷 199-210页

作者： Kaedi, Marjan Ghasem-Aghaee, Nasser Univ Isfahan Dept Comp Engn Esfahan 817467344 Iran

The Case-Based Reasoning (CBR) solves problems by using the past problem solving experiences. How to apply these experiences depends on the type of the problem. The method presented in this paper tries to overcome this difficulty in CBR for optimization problems, using bayesian optimization algorithm (BOA). BOA evolves a population of candidate solutions through constructing bayesian networks and sampling them. After solving the problems through BOA, bayesian networks describing solutions features are obtained. In our method, these bayesian networks are stored in a case-base. For solving a new problem, the bayesian networks of those problems which are similar to the new problem, are retrieved and combined. This compound bayesian network is used for generating the initial population and constructing the probabilistic models of BOA in solving the new problem. Our method improves CBR in two ways: first, in our method, how to use the knowledge stored in the case-base is disregarding the problem itself and is universally;second, this method stores the probabilistic descriptions of the previous solutions in order to make the stored knowledge more flexible. Experimental results showed that in addition to the mentioned advantages, our method improved the solutions quality.

关键词： Otimization case-based reasoning bayesian networks bayesian optimization algorithm

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bayesian Immigrant Diploid Genetic algorithm for Dynamic Environments 1

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14th Biennial International Conference on Artificial Evolution (EA)

作者： Gazioglu, Emrullah Etaner-Uyar, A. Sima Istanbul Tech Univ Istanbul Turkey

ISBN: (数字)9783030457150

ISBN: (纸本)9783030457150

In dynamic environments, the main aim of an optimization algorithm is to track the changes and to adapt the search process. In this paper, we propose an approach called the bayesian Immigrant Diploid Genetic algorithm (BIDGA). BIDGA uses implicit memory in the form of diploid chromosomes, combined with the bayesian optimization algorithm (BOA), which is a form of Estimation of Distribution algorithms (EDAs). Through the use of BOA, BIDGA is able to take into account epistasis in the form of binary relationships between the variables. Experiments show that the proposed approach is efficient and also indicates that exploiting interactions between variables is important to adapt to the newly formed environments.

关键词： Evolutionary algorithms Estimation of Distribution algorithms bayesian optimization algorithm Dynamic environments

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Modeling and Sectional Compensation of Temperature Error of FOG Based on BOA-GBDT

Modeling and Sectional Compensation of Temperature Error of ...

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第33届中国控制与决策会议

作者： Cao Feng Yan Ting-yang Su Min School of Mechanical and Electrical Engineering Qilu Institute of Technology

According to the requirements of temperature compensation for real-time and accuracy,a method of using bayesian algorithm to optimize the gradient boosting tree regression is proposed to establish the temperature error compensation model of fiber optic gyroscope,and it adopts the method of real-time acquisition of temperature change rate with multiple data windows to meet the requirements of online compensation and model *** fiber optic gyroscope is placed in a temperature box to perform a temperature change test of -40-60℃ to obtain measured *** temperature and temperature change rate are used as input,and bayesian algorithm optimization gradient lifting tree regression modeling and temperature rising and falling segment modeling are performed *** comparative experiment results show that the proposed model achieves the best compensation *** the compensation comparison test,it is verified that the proposed model has good compensation ability and generalization ability for non-training data.

关键词： Fiber optic gyroscope Temperature error bayesian optimization algorithm Gradient lifting tree Sectional compensation

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A novel approach to solve AI planning problems in graph transformations

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 92卷 103684-103684页

作者： Pira, Einollah Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz *** Iran

The aim of AI planning is to solve the problems with no exact solution available. These problems usually have a big search space, and planning may not find plans with the least actions and in the shortest time. Recent researches show that using suitable heuristics can help to find desired plans. In planning problems specified formally through graph transformation system (GTS), there are dependencies between applied rules (actions) in the search space. This fact motivates us to solve the planning problem for a small goal (instead of the main goal), extract dependencies from the searched space, and use these dependencies to solve the planning problem for the main goal. In GTS based systems, the nodes of a state (really is a graph) can be grouped due to their type. To create a small (refined) goal, we use a refinement technique to remove the predefined percent of nodes from each group of the main goal. bayesian optimization algorithm (BOA) is then used to solve the planning problem for the refined goal. BOA is an Estimation of Distribution algorithm (EDA) in which bayesian networks are used to evolve the solution populations. Actually, a bayesian network is learned from the current population, and then this network is employed to generate the next population. Since the last bayesian network learned in BOA has the knowledge about dependencies between applied rules, this network can be used to solve the planning problem for the main goal. Experimental results on four well-known planning domains confirm that the proposed approach finds plans with the least actions and in the lower time compared with the state-of-the-art approaches.

关键词： bayesian optimization algorithm AI planning Graph transformation system bayesian network Refinement

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Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning algorithms

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REMOTE SENSING 2020年第23期12卷 3976-3976页

作者： Fiorentini, Nicholas Maboudi, Mehdi Leandri, Pietro Losa, Massimo Gerke, Markus Univ Pisa Dept Civil & Ind Engn Engn Sch Largo Lucio Lazzarino 1 I-56126 Pisa Italy Tech Univ Carolo Wilhelmina Braunschweig Inst Geodesy & Photogrammetry Bienroder Weg 81 D-38106 Braunschweig Germany

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km(2)), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a bayesian optimization algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms;in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose thre

关键词： infrastructure monitoring PS-InSAR machine learning bayesian optimization algorithm classification and regression tree support vector machine boosted regression tree random forest Taylor diagram surface motion

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Using evolutionary algorithms for reachability analysis of complex software systems specified through graph transformation

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 191卷 106577-000页

作者： Pira, Einollah Rafe, Vahid Nikanjam, Amin Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz *** Iran Arak Univ Fac Engn Dept Comp Engn Arak *** Iran KN Toosi Univ Technol Fac Comp Engn Tehran *** Iran

Assessing the reliability of safety-critical systems is an important and challenging task because even a minor failure in these systems may result in catastrophic consequences, like losing human life. A well-known and fully automatic technique in reliability assessing approaches is model checking. However, applying this technique to verify some properties such as safety may lead to the state space explosion problem in which all reachable states cannot be checked due to computational limitations. In such situations that the verification of a safety property is infeasible, it is possible to refute the safety property by searching a reachable state in which a special configuration (e.g., an error or an undesirable behaviour) occurs. Therefore, checking reachability can be done instead of refuting the corresponding safety property. Finding such reachable states, in the worst case, may cause the state space explosion problem again. Hence, using evolutionary algorithms to explore the state space efficiently can be a promising idea. In this paper, at first, we propose an evolutionary algorithm to check reachability properties and refute safety ones in software systems specified formally through graph transformations. Since the accuracy and convergence speed of the proposed approach can still be improved, we employ the bayesian optimization algorithm (BOA) to propose another approach. In BOA, a bayesian network is learnt from the population and then sampled to generate new solutions. The proposed approaches can be used to analyse the reachability and safety properties. The proposed approaches are implemented in GROOVE which is an open source toolset for designing and model checking graph transformation systems. To evaluate the efficiency of the proposed approaches, different benchmark problems are employed. Experimental results show that the proposed approaches are faster and more accurate than the existing methods.

关键词： bayesian optimization algorithm Genetic algorithm Model checking State space explosion Graph transformation system

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Task scheduling using bayesian optimization algorithm for heterogeneous computing environments

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APPLIED SOFT COMPUTING 2011年第4期11卷 3297-3310页

作者： Yang, Jiadong Xu, Hua Pan, Li Jia, Peifa Long, Fei Jie, Ming Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China SIEMENS Ltd Corp Technol Emerging Automat Beijing 100102 Peoples R China

Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platforms, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorithm based on the bayesian optimization algorithm (BOA) for heterogeneous computing environments. In the proposed algorithm, scheduling is divided into two phases. First, according to the task graph of multiprocessor scheduling problems, bayesian networks are initialized and learned to capture the dependencies between different tasks. And the promising solutions assigning tasks to different processors are generated by sampling the bayesian network. Second, the execution sequence of tasks on the same processor is set by the heuristic-based priority used in the list scheduling approach. The proposed algorithm is evaluated and compared with the related approaches by means of the empirical studies on random task graphs and benchmark applications. The experimental results show that the proposed algorithm is able to deliver more efficient schedules. Further experiments indicate that the proposed algorithm maintains almost the same performance with different parameter settings. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Multiprocessor scheduling Heterogeneous Parallel computing bayesian optimization algorithm

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Biasing bayesian optimization algorithm using Case Based Reasoning

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KNOWLEDGE-BASED SYSTEMS 2011年第8期24卷 1245-1253页

作者： Kaedi, Marjan Ghasem-Aghaee, Nasser Univ Isfahan Dept Comp Engn Esfahan 817467344 Iran Sheikh Bahai Univ Esfahan Iran

Studies show that application of the prior knowledge in biasing the Estimation of Distribution algorithms (EDAs), such as bayesian optimization algorithm (BOA), increases the efficiency of these algorithms significantly. One of the main advantages of the EDAs over other optimization algorithms is that the former provides a trail of probabilistic models of candidate solutions with increasing quality. Some recent studies have applied these probabilistic models, obtained from previously solved problems in biasing the BOA algorithm, to solve the future problems. In this paper, in order to improve the previous works and reduce their disadvantages, a method based on Case Based Reasoning (CBR) is proposed for biasing the BOA algorithm. Herein, after running BOA for solving optimization problems, each problem, the corresponding solution, as well as the last bayesian network obtained from the BOA algorithm, will be stored as an entry in the case-base. Upon introducing a new problem, similar problems from the case-base are retrieved and the last bayesian networks of these solved problems are combined according to the degree of their similarity with the new problem;hence, a compound bayesian network is constructed. The compound bayesian network is sampled and the initial population for the BOA algorithm is generated. This network will be applied efficiently for biasing future probabilistic models during the runs of BOA for the new problem. The proposed method is tested on three well-known combinatorial benchmark problems. Experimental results show significant improvements in algorithm execution time and quality of solutions, compared to previous methods. (C) 2011 Elsevier B.V. All rights reserved.

关键词： bayesian optimization algorithm Bias bayesian Network Case Based Reasoning Initial population

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Model accuracy in the bayesian optimization algorithm

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SOFT COMPUTING 2011年第7期15卷 1351-1371页

作者： Lima, Claudio F. Lobo, Fernando G. Pelikan, Martin Goldberg, David E. Univ Nottingham Ctr Plant Integrat Biol Loughborough LE12 5RD Leics England Univ Algarve Dept Elect & Informat Engn P-8000117 Faro Portugal Univ Missouri Dept Math & Comp Sci CCB 320 St Louis MO 63121 USA Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA

Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection operator can lead to model overfitting in bayesian EDAs. Third, the scoring metric that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. Overall, this paper makes a contribution towards understanding and improving model accuracy in BOA, providing more interpretable models to assist efficiency enhancement techniques and human researchers.

关键词： Estimation of distribution algorithms bayesian optimization algorithm bayesian networks Model overfitting Selection

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