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 optimizatio...
详细信息
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.
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 thi...
详细信息
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.
In dynamic environments, the main aim of an optimizationalgorithm 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...
详细信息
ISBN:
(数字)9783030457150
ISBN:
(纸本)9783030457150
In dynamic environments, the main aim of an optimizationalgorithm 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.
According to the requirements of temperature compensation for real-time and accuracy,a method of using bayesianalgorithm to optimize the gradient boosting tree regression is proposed to establish the temperature erro...
详细信息
According to the requirements of temperature compensation for real-time and accuracy,a method of using bayesianalgorithm 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 bayesianalgorithmoptimization 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.
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...
详细信息
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.
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) ...
详细信息
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
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...
详细信息
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.
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 algorith...
详细信息
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.
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 significant...
详细信息
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 optimizationalgorithms 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.
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 ...
详细信息
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.
暂无评论